| Title: | Interface to 'Interpretable AI' Modules |
|---|---|
| Description: | An interface to the algorithms of 'Interpretable AI' <https://www.interpretable.ai> from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available. |
| Authors: | Jack Dunn [aut, cre], Ying Zhuo [aut], Interpretable AI LLC [cph] |
| Maintainer: | Jack Dunn <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.10.2 |
| Built: | 2024-10-19 03:28:11 UTC |
| Source: | CRAN |
Julia Equivalent:
IAI.acquire_license
acquire_license(...)acquire_license(...)
... |
Refer to the Julia documentation for available parameters |
Requires IAI version 3.1 or higher.
## Not run: iai::acquire_license()## Not run: iai::acquire_license()
Julia Equivalent:
Distributed.addprocs!
add_julia_processes(...)add_julia_processes(...)
... |
Refer to the Julia documentation for available parameters |
For more information, refer to the documentation on parallelization
## Not run: iai::add_julia_processes(3)## Not run: iai::add_julia_processes(3)
Julia Equivalent:
IAI.all_treatment_combinations
all_treatment_combinations(...)all_treatment_combinations(...)
... |
A vector of possible options for each treatment |
## Not run: iai::all_treatment_combinations(c(1, 2, 3))## Not run: iai::all_treatment_combinations(c(1, 2, 3))
Julia Equivalent:
IAI.apply
apply(lnr, X)apply(lnr, X)
lnr |
The learner or grid to query. |
X |
The features of the data. |
## Not run: iai::apply(lnr, X)## Not run: iai::apply(lnr, X)
Julia Equivalent:
IAI.apply_nodes
apply_nodes(lnr, X)apply_nodes(lnr, X)
lnr |
The learner or grid to query. |
X |
The features of the data. |
## Not run: iai::apply_nodes(lnr, X)## Not run: iai::apply_nodes(lnr, X)
Julia Equivalent:
IAI.make_mixed_data
as.mixeddata(values, categorical_levels, ordinal_levels = c())as.mixeddata(values, categorical_levels, ordinal_levels = c())
values |
The vector of values to convert |
categorical_levels |
The values in |
ordinal_levels |
(optional) The values in |
## Not run: df <- iris set.seed(1) df$mixed <- rnorm(150) df$mixed[1:5] <- NA # Insert some missing values df$mixed[6:10] <- "Not graded" df$mixed <- iai::as.mixeddata(df$mixed, c("Not graded")) ## End(Not run)## Not run: df <- iris set.seed(1) df$mixed <- rnorm(150) df$mixed[1:5] <- NA # Insert some missing values df$mixed[6:10] <- "Not graded" df$mixed <- iai::as.mixeddata(df$mixed, c("Not graded")) ## End(Not run)
ggplot2::ggplot
object plotting grid search results for Optimal Feature Selection learnersConstruct a
ggplot2::ggplot
object plotting grid search results for Optimal Feature Selection learners
## S3 method for class 'grid_search' autoplot(object, type = stop("`type` is required"), ...)## S3 method for class 'grid_search' autoplot(object, type = stop("`type` is required"), ...)
object |
The grid search to plot |
type |
The type of plot to construct (either |
... |
Additional arguments (unused) |
Requires IAI version 2.2 or higher.
## Not run: ggplot2::autoplot(grid)## Not run: ggplot2::autoplot(grid)
ggplot2::ggplot object plotting the ROC curveConstruct a ggplot2::ggplot object plotting the ROC curve
## S3 method for class 'roc_curve' autoplot(object, ...)## S3 method for class 'roc_curve' autoplot(object, ...)
object |
The ROC curve to plot |
... |
Additional arguments (unused) |
Requires IAI version 2.1 or higher.
## Not run: ggplot2::autoplot(roc)## Not run: ggplot2::autoplot(roc)
ggplot2::ggplot object plotting the results of the similarity comparisonConstruct a ggplot2::ggplot object plotting the results of the similarity comparison
## S3 method for class 'similarity_comparison' autoplot(object, ...)## S3 method for class 'similarity_comparison' autoplot(object, ...)
object |
The similarity comparison to plot |
... |
Additional arguments (unused) |
Requires IAI version 2.2 or higher.
## Not run: ggplot2::autoplot(similarity)## Not run: ggplot2::autoplot(similarity)
ggplot2::ggplot object plotting the results of the stability analysisConstruct a ggplot2::ggplot object plotting the results of the stability analysis
## S3 method for class 'stability_analysis' autoplot(object, ...)## S3 method for class 'stability_analysis' autoplot(object, ...)
object |
The stability analysis to plot |
... |
Additional arguments (unused) |
Requires IAI version 2.2 or higher.
## Not run: ggplot2::autoplot(stability)## Not run: ggplot2::autoplot(stability)
Julia Equivalent:
IAI.CategoricalClassificationRewardEstimator
categorical_classification_reward_estimator(...)categorical_classification_reward_estimator(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.2 or higher.
## Not run: lnr <- iai::categorical_classification_reward_estimator()## Not run: lnr <- iai::categorical_classification_reward_estimator()
Julia Equivalent:
IAI.CategoricalRegressionRewardEstimator
categorical_regression_reward_estimator(...)categorical_regression_reward_estimator(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.2 or higher.
## Not run: lnr <- iai::categorical_regression_reward_estimator()## Not run: lnr <- iai::categorical_regression_reward_estimator()
This function was deprecated in iai 1.6.0, and [categorical_classification_reward_estimator()] or [categorical_classification_reward_estimator()] should be used instead.
categorical_reward_estimator(...)categorical_reward_estimator(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
This deprecation is no longer supported as of the IAI v3 release.
Requires IAI version 2.0, 2.1 or 2.2.
## Not run: lnr <- iai::categorical_reward_estimator()## Not run: lnr <- iai::categorical_reward_estimator()
Julia Equivalent:
IAI.CategoricalSurvivalRewardEstimator
categorical_survival_reward_estimator(...)categorical_survival_reward_estimator(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.2 or higher.
## Not run: lnr <- iai::categorical_survival_reward_estimator()## Not run: lnr <- iai::categorical_survival_reward_estimator()
Removes files created by install_julia and
install_system_image
cleanup_installation()cleanup_installation()
## Not run: iai::cleanup_installation()## Not run: iai::cleanup_installation()
Julia Equivalent:
IAI.clone
clone(lnr)clone(lnr)
lnr |
The learner to copy. |
## Not run: new_lnr <- iai::clone(lnr)## Not run: new_lnr <- iai::clone(lnr)
Julia Equivalent:
IAI.convert_treatments_to_numeric
convert_treatments_to_numeric(treatments)convert_treatments_to_numeric(treatments)
treatments |
The treatments to convert |
## Not run: iai::convert_treatments_to_numeric(c("1", "2", "3"))## Not run: iai::convert_treatments_to_numeric(c("1", "2", "3"))
Julia Equivalent:
IAI.copy_splits_and_refit_leaves!
copy_splits_and_refit_leaves(new_lnr, orig_lnr, ...)copy_splits_and_refit_leaves(new_lnr, orig_lnr, ...)
new_lnr |
The learner to modify and refit |
orig_lnr |
The learner from which to copy the tree split structure |
... |
Refer to the Julia documentation for available parameters |
Requires IAI version 3.0 or higher.
## Not run: iai::copy_splits_and_refit_leaves(new_lnr, orig_lnr, ...)## Not run: iai::copy_splits_and_refit_leaves(new_lnr, orig_lnr, ...)
(i, j) is true if the ith point in
the features passes through the jth node in a trained tree model.Julia Equivalent:
IAI.decision_path
decision_path(lnr, X)decision_path(lnr, X)
lnr |
The learner or grid to query. |
X |
The features of the data. |
## Not run: iai::decision_path(lnr, X)## Not run: iai::decision_path(lnr, X)
Julia Equivalent:
IAI.delete_rich_output_param!
delete_rich_output_param(key)delete_rich_output_param(key)
key |
The parameter to delete. |
## Not run: iai::delete_rich_output_param("simple_layout")## Not run: iai::delete_rich_output_param("simple_layout")
For use with data from randomized experiments where treatments are known to be randomly assigned.
equal_propensity_estimator(...)equal_propensity_estimator(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Julia Equivalent:
IAI.EqualPropensityEstimator
Requires IAI version 2.1 or higher.
## Not run: lnr <- iai::equal_propensity_estimator()## Not run: lnr <- iai::equal_propensity_estimator()
Generic function for fitting a learner.
fit(obj, ...)fit(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.fit_and_expand!
fit_and_expand(lnr, X, ...)fit_and_expand(lnr, X, ...)
lnr |
The learner to use for imputation. |
X |
The dataframe in which to impute missing values. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.0 or higher.
## Not run: lnr <- iai::fit_and_expand(lnr, X, type = "finite")## Not run: lnr <- iai::fit_and_expand(lnr, X, type = "finite")
Julia Equivalent:
IAI.fit_cv!
fit_cv(grid, X, ...)fit_cv(grid, X, ...)
grid |
The grid to fit. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters. |
## Not run: X <- iris[, 1:4] y <- iris$Species grid <- iai::grid_search( iai::optimal_tree_classifier(max_depth = 1), ) iai::fit_cv(grid, X, y) ## End(Not run)## Not run: X <- iris[, 1:4] y <- iris$Species grid <- iai::grid_search( iai::optimal_tree_classifier(max_depth = 1), ) iai::fit_cv(grid, X, y) ## End(Not run)
Julia Equivalent:
IAI.fit_predict!
fit_predict(obj, ...)fit_predict(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.fit_predict!
## S3 method for class 'categorical_reward_estimator' fit_predict(obj, X, treatments, ...)## S3 method for class 'categorical_reward_estimator' fit_predict(obj, X, treatments, ...)
obj |
The learner or grid to use for estimation |
X |
The features of the data. |
treatments |
The treatment applied to each point in the data. |
... |
Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information. |
Requires IAI version 2.0 or higher.
## Not run: iai::fit_predict(obj, X, treatments, outcomes)## Not run: iai::fit_predict(obj, X, treatments, outcomes)
Julia Equivalent:
IAI.fit_predict!
## S3 method for class 'numeric_reward_estimator' fit_predict(obj, X, treatments, ...)## S3 method for class 'numeric_reward_estimator' fit_predict(obj, X, treatments, ...)
obj |
The learner or grid to use for estimation |
X |
The features of the data. |
treatments |
The treatment applied to each point in the data. |
... |
Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information. |
Requires IAI version 2.1 or higher.
## Not run: iai::fit_predict(obj, X, treatments, outcomes)## Not run: iai::fit_predict(obj, X, treatments, outcomes)
Similar to calling
fit.imputation_learner
followed by
transform
fit_transform(lnr, X, ...)fit_transform(lnr, X, ...)
lnr |
The learner or grid to use for imputation |
X |
The features of the data. |
... |
Refer to the Julia documentation for available parameters. |
Julia Equivalent:
IAI.fit_transform!
## Not run: X <- iris X[1, 1] <- NA grid <- iai::grid_search( iai::imputation_learner(), method = c("opt_knn", "opt_tree"), ) iai::fit_transform(grid, X) ## End(Not run)## Not run: X <- iris X[1, 1] <- NA grid <- iai::grid_search( iai::imputation_learner(), method = c("opt_knn", "opt_tree"), ) iai::fit_transform(grid, X) ## End(Not run)
Julia Equivalent:
IAI.fit_transform_cv!
fit_transform_cv(grid, X, ...)fit_transform_cv(grid, X, ...)
grid |
The grid to use for imputation |
X |
The features of the data. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: X <- iris X[1, 1] <- NA grid <- iai::grid_search( iai::imputation_learner(), method = c("opt_knn", "opt_tree"), ) iai::fit_transform_cv(grid, X) ## End(Not run)## Not run: X <- iris X[1, 1] <- NA grid <- iai::grid_search( iai::imputation_learner(), method = c("opt_knn", "opt_tree"), ) iai::fit_transform_cv(grid, X) ## End(Not run)
grid_search to the training dataJulia Equivalent:
IAI.fit!
## S3 method for class 'grid_search' fit(obj, X, ...)## S3 method for class 'grid_search' fit(obj, X, ...)
obj |
The grid search to fit. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters. |
## Not run: X <- iris[, 1:4] y <- iris$Species grid <- iai::grid_search( iai::optimal_tree_classifier(max_depth = 1), ) iai::fit(grid, X, y) ## End(Not run)## Not run: X <- iris[, 1:4] y <- iris$Species grid <- iai::grid_search( iai::optimal_tree_classifier(max_depth = 1), ) iai::fit(grid, X, y) ## End(Not run)
Additional keyword arguments are available for fitting imputation learners - please refer to the Julia documentation.
## S3 method for class 'imputation_learner' fit(obj, X, ...)## S3 method for class 'imputation_learner' fit(obj, X, ...)
obj |
The learner or grid to fit. |
X |
The features of the data. |
... |
Refer to the Julia documentation for available parameters. |
Julia Equivalent:
IAI.fit!
## Not run: iai::fit(lnr, X)## Not run: iai::fit(lnr, X)
Julia Equivalent:
IAI.fit!
## S3 method for class 'learner' fit(obj, X, ...)## S3 method for class 'learner' fit(obj, X, ...)
obj |
The learner to fit. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters. |
## Not run: iai::fit(lnr, X, y)## Not run: iai::fit(lnr, X, y)
When the coordinated_sparsity parameter of the learner is TRUE,
additional keyword arguments are required - please refer to the Julia
documentation.
## S3 method for class 'optimal_feature_selection_learner' fit(obj, X, ...)## S3 method for class 'optimal_feature_selection_learner' fit(obj, X, ...)
obj |
The learner or grid to fit. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters. |
Julia Equivalent:
IAI.fit!
Requires IAI version 1.1 or higher.
## Not run: iai::fit(lnr, X)## Not run: iai::fit(lnr, X)
Julia Equivalent:
IAI.get_best_params
get_best_params(grid)get_best_params(grid)
grid |
The grid search to query. |
## Not run: iai::get_best_params(grid)## Not run: iai::get_best_params(grid)
Generic function for returning the predicted label in the node of a classification tree
get_classification_label(obj, ...)get_classification_label(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.get_classification_label
## S3 method for class 'classification_tree_learner' get_classification_label(obj, node_index, ...)## S3 method for class 'classification_tree_learner' get_classification_label(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::get_classification_label(lnr, 1)## Not run: iai::get_classification_label(lnr, 1)
Julia Equivalent:
IAI.get_classification_label
and
IAI.get_classification_label
## S3 method for class 'classification_tree_multi_learner' get_classification_label(obj, node_index, ...)## S3 method for class 'classification_tree_multi_learner' get_classification_label(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: iai::get_classification_label(lnr, 1)## Not run: iai::get_classification_label(lnr, 1)
Generic function for returning the probabilities of class membership at a node of a classification tree
get_classification_proba(obj, ...)get_classification_proba(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.get_classification_proba
## S3 method for class 'classification_tree_learner' get_classification_proba(obj, node_index, ...)## S3 method for class 'classification_tree_learner' get_classification_proba(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::get_classification_proba(lnr, 1)## Not run: iai::get_classification_proba(lnr, 1)
Julia Equivalent:
IAI.get_classification_proba
and
IAI.get_classification_proba
## S3 method for class 'classification_tree_multi_learner' get_classification_proba(obj, node_index, ...)## S3 method for class 'classification_tree_multi_learner' get_classification_proba(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: iai::get_classification_proba(lnr, 1)## Not run: iai::get_classification_proba(lnr, 1)
Julia Equivalent:
IAI.get_cluster_assignments
get_cluster_assignments(stability, num_trees)get_cluster_assignments(stability, num_trees)
stability |
The stability analysis to query |
num_trees |
The number of trees to include in the clustering |
Requires IAI version 2.2 or higher.
## Not run: iai::get_cluster_assignments(stability, num_trees)## Not run: iai::get_cluster_assignments(stability, num_trees)
Julia Equivalent:
IAI.get_cluster_details
get_cluster_details(stability, num_trees)get_cluster_details(stability, num_trees)
stability |
The stability analysis to query |
num_trees |
The number of trees to include in the clustering |
Requires IAI version 2.2 or higher.
## Not run: iai::get_cluster_details(stability, num_trees)## Not run: iai::get_cluster_details(stability, num_trees)
Julia Equivalent:
IAI.get_cluster_distances
get_cluster_distances(stability, num_trees)get_cluster_distances(stability, num_trees)
stability |
The stability analysis to query |
num_trees |
The number of trees to include in the clustering |
Requires IAI version 2.2 or higher.
## Not run: iai::get_cluster_distances(stability, num_trees)## Not run: iai::get_cluster_distances(stability, num_trees)
Julia Equivalent:
IAI.get_depth
get_depth(lnr, node_index)get_depth(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::get_depth(lnr, 1)## Not run: iai::get_depth(lnr, 1)
Julia Equivalent:
IAI.get_estimation_densities
get_estimation_densities(lnr, ...)get_estimation_densities(lnr, ...)
lnr |
The learner from which to extract densities |
... |
Refer to the Julia documentation for other parameters |
Requires IAI version 2.2 or higher.
## Not run: iai::get_estimation_densities(lnr, ...)## Not run: iai::get_estimation_densities(lnr, ...)
Julia Equivalent:
IAI.get_features_used
get_features_used(lnr)get_features_used(lnr)
lnr |
The learner to query. |
Requires IAI version 2.2 or higher.
## Not run: iai::get_features_used(lnr)## Not run: iai::get_features_used(lnr)
Julia Equivalent:
IAI.get_grid_result_details
get_grid_result_details(grid)get_grid_result_details(grid)
grid |
The grid search to query. |
Requires IAI version 2.2 or higher.
## Not run: iai::get_grid_result_details(grid)## Not run: iai::get_grid_result_details(grid)
Julia Equivalent:
IAI.get_grid_result_summary
get_grid_result_summary(grid)get_grid_result_summary(grid)
grid |
The grid search to query. |
## Not run: iai::get_grid_result_summary(grid)## Not run: iai::get_grid_result_summary(grid)
This function was deprecated and renamed to [get_grid_result_summary()] in iai 1.5.0. This is for consistency with the IAI v2.2.0 Julia release.
get_grid_results(grid)get_grid_results(grid)
grid |
The grid search to query. |
## Not run: iai::get_grid_results(grid)## Not run: iai::get_grid_results(grid)
Julia Equivalent:
IAI.get_learner
get_learner(grid)get_learner(grid)
grid |
The grid to query. |
## Not run: lnr <- iai::get_learner(grid)## Not run: lnr <- iai::get_learner(grid)
Julia Equivalent:
IAI.get_lower_child
get_lower_child(lnr, node_index)get_lower_child(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::get_lower_child(lnr, 1)## Not run: iai::get_lower_child(lnr, 1)
This ID ties the IAI license file to your machine.
get_machine_id()get_machine_id()
## Not run: iai::get_machine_id()## Not run: iai::get_machine_id()
Generic function for returning the number of fits in a trained learner
get_num_fits(obj, ...)get_num_fits(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.get_num_fits
## S3 method for class 'glmnetcv_learner' get_num_fits(obj, ...)## S3 method for class 'glmnetcv_learner' get_num_fits(obj, ...)
obj |
The GLMNet learner to query. |
... |
Additional arguments (unused) |
Requires IAI version 2.1 or higher.
## Not run: lnr <- iai::get_num_fits(lnr)## Not run: lnr <- iai::get_num_fits(lnr)
Julia Equivalent:
IAI.get_num_fits
## S3 method for class 'optimal_feature_selection_learner' get_num_fits(obj, ...)## S3 method for class 'optimal_feature_selection_learner' get_num_fits(obj, ...)
obj |
The Optimal Feature Selection learner to query. |
... |
Additional arguments (unused) |
Requires IAI version 3.0 or higher.
## Not run: iai::get_num_fits(lnr)## Not run: iai::get_num_fits(lnr)
Julia Equivalent:
IAI.get_num_nodes
get_num_nodes(lnr)get_num_nodes(lnr)
lnr |
The learner to query. |
## Not run: iai::get_num_nodes(lnr)## Not run: iai::get_num_nodes(lnr)
Julia Equivalent:
IAI.get_num_samples
get_num_samples(lnr, node_index)get_num_samples(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::get_num_samples(lnr, 1)## Not run: iai::get_num_samples(lnr, 1)
Julia Equivalent:
IAI.get_params
get_params(lnr)get_params(lnr)
lnr |
The learner to query. |
## Not run: iai::get_params(lnr)## Not run: iai::get_params(lnr)
Julia Equivalent:
IAI.get_parent
get_parent(lnr, node_index)get_parent(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::get_parent(lnr, 2)## Not run: iai::get_parent(lnr, 2)
Julia Equivalent:
IAI.get_policy_treatment_outcome
get_policy_treatment_outcome(lnr, node_index, ...)get_policy_treatment_outcome(lnr, node_index, ...)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: iai::get_policy_treatment_outcome(lnr, 1)## Not run: iai::get_policy_treatment_outcome(lnr, 1)
Julia Equivalent:
IAI.get_policy_treatment_outcome_standard_error
get_policy_treatment_outcome_standard_error(lnr, node_index, ...)get_policy_treatment_outcome_standard_error(lnr, node_index, ...)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: iai::get_policy_treatment_outcome_standard_error(lnr, 1)## Not run: iai::get_policy_treatment_outcome_standard_error(lnr, 1)
Julia Equivalent:
IAI.get_policy_treatment_rank
get_policy_treatment_rank(lnr, node_index, ...)get_policy_treatment_rank(lnr, node_index, ...)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 2.0 or higher.
## Not run: iai::get_policy_treatment_rank(lnr, 1)## Not run: iai::get_policy_treatment_rank(lnr, 1)
Generic function for returning the prediction constant in a trained learner
get_prediction_constant(obj, ...)get_prediction_constant(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.get_prediction_constant
## S3 method for class 'glmnetcv_learner' get_prediction_constant(obj, fit_index = NULL, ...)## S3 method for class 'glmnetcv_learner' get_prediction_constant(obj, fit_index = NULL, ...)
obj |
The learner to query. |
fit_index |
The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied. |
... |
Additional arguments (unused) |
Requires IAI version 2.1 or higher.
## Not run: iai::get_prediction_constant(lnr)## Not run: iai::get_prediction_constant(lnr)
Julia Equivalent:
IAI.get_prediction_constant
## S3 method for class 'optimal_feature_selection_learner' get_prediction_constant(obj, fit_index = NULL, ...)## S3 method for class 'optimal_feature_selection_learner' get_prediction_constant(obj, fit_index = NULL, ...)
obj |
The learner to query. |
fit_index |
The index of the cluster to use for prediction, if the
|
... |
Additional arguments (unused) |
Requires IAI version 1.1 or higher.
## Not run: iai::get_prediction_constant(lnr)## Not run: iai::get_prediction_constant(lnr)
Generic function for returning the prediction weights in a trained learner
get_prediction_weights(obj, ...)get_prediction_weights(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.get_prediction_weights
## S3 method for class 'glmnetcv_learner' get_prediction_weights(obj, fit_index = NULL, ...)## S3 method for class 'glmnetcv_learner' get_prediction_weights(obj, fit_index = NULL, ...)
obj |
The learner to query. |
fit_index |
The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied. |
... |
Additional arguments (unused) |
Requires IAI version 2.1 or higher.
## Not run: iai::get_prediction_weights(lnr)## Not run: iai::get_prediction_weights(lnr)
Julia Equivalent:
IAI.get_prediction_weights
## S3 method for class 'optimal_feature_selection_learner' get_prediction_weights(obj, fit_index = NULL, ...)## S3 method for class 'optimal_feature_selection_learner' get_prediction_weights(obj, fit_index = NULL, ...)
obj |
The learner to query. |
fit_index |
The index of the cluster to use for prediction, if the
|
... |
Additional arguments (unused) |
Requires IAI version 1.1 or higher.
## Not run: iai::get_prediction_weights(lnr)## Not run: iai::get_prediction_weights(lnr)
Julia Equivalent:
IAI.get_prescription_treatment_rank
get_prescription_treatment_rank(lnr, node_index, ...)get_prescription_treatment_rank(lnr, node_index, ...)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::get_prescription_treatment_rank(lnr, 1)## Not run: iai::get_prescription_treatment_rank(lnr, 1)
Generic function for returning the constant term in the regression prediction at a node of a tree
get_regression_constant(obj, ...)get_regression_constant(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.get_regression_constant
## S3 method for class 'classification_tree_learner' get_regression_constant(obj, node_index, ...)## S3 method for class 'classification_tree_learner' get_regression_constant(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.0 or higher.
## Not run: iai::get_regression_constant(lnr, 1)## Not run: iai::get_regression_constant(lnr, 1)
Julia Equivalent:
IAI.get_regression_constant
and
IAI.get_regression_constant
## S3 method for class 'classification_tree_multi_learner' get_regression_constant(obj, node_index, ...)## S3 method for class 'classification_tree_multi_learner' get_regression_constant(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: iai::get_regression_constant(lnr, 1)## Not run: iai::get_regression_constant(lnr, 1)
Julia Equivalent:
IAI.get_regression_constant
## S3 method for class 'prescription_tree_learner' get_regression_constant(obj, node_index, treatment, ...)## S3 method for class 'prescription_tree_learner' get_regression_constant(obj, node_index, treatment, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
treatment |
The treatment to query. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::get_regression_constant(lnr, 1, "A")## Not run: iai::get_regression_constant(lnr, 1, "A")
Julia Equivalent:
IAI.get_regression_constant
## S3 method for class 'regression_tree_learner' get_regression_constant(obj, node_index, ...)## S3 method for class 'regression_tree_learner' get_regression_constant(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::get_regression_constant(lnr, 1)## Not run: iai::get_regression_constant(lnr, 1)
Julia Equivalent:
IAI.get_regression_constant
and
IAI.get_regression_constant
## S3 method for class 'regression_tree_multi_learner' get_regression_constant(obj, node_index, ...)## S3 method for class 'regression_tree_multi_learner' get_regression_constant(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: iai::get_regression_constant(lnr, 1)## Not run: iai::get_regression_constant(lnr, 1)
Julia Equivalent:
IAI.get_regression_constant
## S3 method for class 'survival_tree_learner' get_regression_constant(obj, node_index, ...)## S3 method for class 'survival_tree_learner' get_regression_constant(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.0 or higher.
## Not run: iai::get_regression_constant(lnr, 1)## Not run: iai::get_regression_constant(lnr, 1)
Generic function for returning the weights for each feature in the regression prediction at a node of a tree
get_regression_weights(obj, ...)get_regression_weights(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.get_regression_weights
## S3 method for class 'classification_tree_learner' get_regression_weights(obj, node_index, ...)## S3 method for class 'classification_tree_learner' get_regression_weights(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.0 or higher.
## Not run: iai::get_regression_weights(lnr, 1)## Not run: iai::get_regression_weights(lnr, 1)
Julia Equivalent:
IAI.get_regression_weights
and
IAI.get_regression_weights
## S3 method for class 'classification_tree_multi_learner' get_regression_weights(obj, node_index, ...)## S3 method for class 'classification_tree_multi_learner' get_regression_weights(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: iai::get_regression_weights(lnr, 1)## Not run: iai::get_regression_weights(lnr, 1)
Julia Equivalent:
IAI.get_regression_weights
## S3 method for class 'prescription_tree_learner' get_regression_weights(obj, node_index, treatment, ...)## S3 method for class 'prescription_tree_learner' get_regression_weights(obj, node_index, treatment, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
treatment |
The treatment to query. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::get_regression_weights(lnr, 1, "A")## Not run: iai::get_regression_weights(lnr, 1, "A")
Julia Equivalent:
IAI.get_regression_weights
## S3 method for class 'regression_tree_learner' get_regression_weights(obj, node_index, ...)## S3 method for class 'regression_tree_learner' get_regression_weights(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::get_regression_weights(lnr, 1)## Not run: iai::get_regression_weights(lnr, 1)
Julia Equivalent:
IAI.get_regression_weights
and
IAI.get_regression_weights
## S3 method for class 'regression_tree_multi_learner' get_regression_weights(obj, node_index, ...)## S3 method for class 'regression_tree_multi_learner' get_regression_weights(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: iai::get_regression_weights(lnr, 1)## Not run: iai::get_regression_weights(lnr, 1)
Julia Equivalent:
IAI.get_regression_weights
## S3 method for class 'survival_tree_learner' get_regression_weights(obj, node_index, ...)## S3 method for class 'survival_tree_learner' get_regression_weights(obj, node_index, ...)
obj |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.0 or higher.
## Not run: iai::get_regression_weights(lnr, 1)## Not run: iai::get_regression_weights(lnr, 1)
Julia Equivalent:
IAI.get_rich_output_params
get_rich_output_params()get_rich_output_params()
## Not run: iai::get_rich_output_params()## Not run: iai::get_rich_output_params()
ROC curves are returned by roc_curve, e.g.
roc_curve.classification_learner
get_roc_curve_data(curve)get_roc_curve_data(curve)
curve |
The curve to query. |
The data is returned as a list with two keys: auc giving the
area-under-the-curve, and coords containing a vector of lists
representing each point on the curve, each with keys fpr (the false
positive rate), tpr (the true positive rate) and threshold
(the threshold).
Julia Equivalent:
IAI.get_roc_curve_data
Requires IAI version 2.1 or higher.
## Not run: iai::get_roc_curve_data(curve)## Not run: iai::get_roc_curve_data(curve)
Julia Equivalent:
IAI.get_split_categories
get_split_categories(lnr, node_index)get_split_categories(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::get_split_categories(lnr, 1)## Not run: iai::get_split_categories(lnr, 1)
Julia Equivalent:
IAI.get_split_feature
get_split_feature(lnr, node_index)get_split_feature(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::get_split_feature(lnr, 1)## Not run: iai::get_split_feature(lnr, 1)
Julia Equivalent:
IAI.get_split_threshold
get_split_threshold(lnr, node_index)get_split_threshold(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::get_split_threshold(lnr, 1)## Not run: iai::get_split_threshold(lnr, 1)
Julia Equivalent:
IAI.get_split_weights
get_split_weights(lnr, node_index)get_split_weights(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::get_split_weights(lnr, 1)## Not run: iai::get_split_weights(lnr, 1)
Julia Equivalent:
IAI.get_stability_results
get_stability_results(stability)get_stability_results(stability)
stability |
The stability analysis to query |
Requires IAI version 2.2 or higher.
## Not run: iai::get_stability_results(stability)## Not run: iai::get_stability_results(stability)
Julia Equivalent:
IAI.get_survival_curve
get_survival_curve(lnr, node_index, ...)get_survival_curve(lnr, node_index, ...)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::get_survival_curve(lnr, 1)## Not run: iai::get_survival_curve(lnr, 1)
predict.survival_learner
or
get_survival_curve)The data is returned as a list with two keys: times containing the
time for each breakpoint on the curve, and coefs containing the
probability for each breakpoint on the curve.
get_survival_curve_data(curve)get_survival_curve_data(curve)
curve |
The curve to query. |
Julia Equivalent:
IAI.get_survival_curve_data
## Not run: iai::get_survival_curve_data(curve)## Not run: iai::get_survival_curve_data(curve)
Julia Equivalent:
IAI.get_survival_expected_time
get_survival_expected_time(lnr, node_index, ...)get_survival_expected_time(lnr, node_index, ...)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: iai::get_survival_expected_time(lnr, 1)## Not run: iai::get_survival_expected_time(lnr, 1)
Julia Equivalent:
IAI.get_survival_hazard
get_survival_hazard(lnr, node_index, ...)get_survival_hazard(lnr, node_index, ...)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: iai::get_survival_hazard(lnr, 1)## Not run: iai::get_survival_hazard(lnr, 1)
Julia Equivalent:
IAI.get_train_errors
get_train_errors(similarity)get_train_errors(similarity)
similarity |
The similarity comparison |
Requires IAI version 2.2 or higher.
## Not run: iai::get_train_errors(similarity)## Not run: iai::get_train_errors(similarity)
Julia Equivalent:
IAI.get_tree
get_tree(lnr, index)get_tree(lnr, index)
lnr |
The original learner |
index |
The index of the tree to use |
Requires IAI version 2.2 or higher.
## Not run: iai::get_tree(lnr, index)## Not run: iai::get_tree(lnr, index)
Julia Equivalent:
IAI.get_upper_child
get_upper_child(lnr, node_index)get_upper_child(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::get_upper_child(lnr, 1)## Not run: iai::get_upper_child(lnr, 1)
Julia Equivalent:
IAI.GLMNetCVClassifier
glmnetcv_classifier(...)glmnetcv_classifier(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 3.0 or higher.
## Not run: lnr <- iai::glmnetcv_classifier()## Not run: lnr <- iai::glmnetcv_classifier()
Julia Equivalent:
IAI.GLMNetCVRegressor
glmnetcv_regressor(...)glmnetcv_regressor(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: lnr <- iai::glmnetcv_regressor()## Not run: lnr <- iai::glmnetcv_regressor()
Julia Equivalent:
IAI.GLMNetCVSurvivalLearner
glmnetcv_survival_learner(...)glmnetcv_survival_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 3.0 or higher.
## Not run: lnr <- iai::glmnetcv_survival_learner()## Not run: lnr <- iai::glmnetcv_survival_learner()
Julia Equivalent:
IAI.GridSearch
grid_search(lnr, ...)grid_search(lnr, ...)
lnr |
The learner to use when validating. |
... |
The parameters to validate over. |
## Not run: grid <- iai::grid_search( iai::optimal_tree_classifier( random_seed = 1, ), max_depth = 1:5, ) ## End(Not run)## Not run: grid <- iai::grid_search( iai::optimal_tree_classifier( random_seed = 1, ), max_depth = 1:5, ) ## End(Not run)
This function is called automatically with default parameters the first time any 'iai' function is used in an R session. If custom parameters for Julia setup are required, this function must be called in every R session before calling other 'iai' functions.
iai_setup(...)iai_setup(...)
... |
All parameters are passed through to
|
## Not run: iai::iai_setup()## Not run: iai::iai_setup()
Julia Equivalent:
IAI.ImputationLearner
imputation_learner(method = "opt_knn", ...)imputation_learner(method = "opt_knn", ...)
method |
(optional) Specifies the imputation method to use. |
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::imputation_learner(method = "opt_tree")## Not run: lnr <- iai::imputation_learner(method = "opt_tree")
Julia Equivalent:
IAI.impute
impute(X, ...)impute(X, ...)
X |
The dataframe in which to impute missing values. |
... |
Refer to the Julia documentation for available parameters. |
This function was deprecated in iai 1.7.0. This is for consistency with the IAI v3.0.0 Julia release.
## Not run: X <- iris X[1, 1] <- NA iai::impute(X) ## End(Not run)## Not run: X <- iris X[1, 1] <- NA iai::impute(X) ## End(Not run)
Julia Equivalent:
IAI.impute_cv
impute_cv(X, ...)impute_cv(X, ...)
X |
The dataframe in which to impute missing values. |
... |
Refer to the Julia documentation for available parameters. |
This function was deprecated in iai 1.7.0. This is for consistency with the IAI v3.0.0 Julia release.
## Not run: X <- iris X[1, 1] <- NA iai::impute_cv(X, list(method = c("opt_knn", "opt_tree"))) ## End(Not run)## Not run: X <- iris X[1, 1] <- NA iai::impute_cv(X, list(method = c("opt_knn", "opt_tree"))) ## End(Not run)
Download and install Julia automatically.
install_julia(version = "latest", prefix = julia_default_install_dir())install_julia(version = "latest", prefix = julia_default_install_dir())
version |
The version of Julia to install (e.g. |
prefix |
The directory where Julia will be installed. Defaults to a
location determined by
|
## Not run: iai::install_julia()## Not run: iai::install_julia()
Download and install the IAI system image automatically.
install_system_image( version = "latest", replace_default = FALSE, prefix = sysimage_default_install_dir(), accept_license = FALSE )install_system_image( version = "latest", replace_default = FALSE, prefix = sysimage_default_install_dir(), accept_license = FALSE )
version |
The version of the IAI system image to install (e.g.
|
replace_default |
Whether to replace the default Julia system image with
the downloaded IAI system image. Defaults to
|
prefix |
The directory where the IAI system image will be installed.
Defaults to a location determined by
|
accept_license |
Set to |
## Not run: iai::install_system_image()## Not run: iai::install_system_image()
Julia Equivalent:
IAI.is_categoric_split
is_categoric_split(lnr, node_index)is_categoric_split(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::is_categoric_split(lnr, 1)## Not run: iai::is_categoric_split(lnr, 1)
Julia Equivalent:
IAI.is_hyperplane_split
is_hyperplane_split(lnr, node_index)is_hyperplane_split(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::is_hyperplane_split(lnr, 1)## Not run: iai::is_hyperplane_split(lnr, 1)
Julia Equivalent:
IAI.is_leaf
is_leaf(lnr, node_index)is_leaf(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::is_leaf(lnr, 1)## Not run: iai::is_leaf(lnr, 1)
Julia Equivalent:
IAI.is_mixed_ordinal_split
is_mixed_ordinal_split(lnr, node_index)is_mixed_ordinal_split(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::is_mixed_ordinal_split(lnr, 1)## Not run: iai::is_mixed_ordinal_split(lnr, 1)
Julia Equivalent:
IAI.is_mixed_parallel_split
is_mixed_parallel_split(lnr, node_index)is_mixed_parallel_split(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::is_mixed_parallel_split(lnr, 1)## Not run: iai::is_mixed_parallel_split(lnr, 1)
Julia Equivalent:
IAI.is_ordinal_split
is_ordinal_split(lnr, node_index)is_ordinal_split(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::is_ordinal_split(lnr, 1)## Not run: iai::is_ordinal_split(lnr, 1)
Julia Equivalent:
IAI.is_parallel_split
is_parallel_split(lnr, node_index)is_parallel_split(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::is_parallel_split(lnr, 1)## Not run: iai::is_parallel_split(lnr, 1)
The library will be installed if not already present.
load_graphviz()load_graphviz()
## Not run: iai::load_graphviz()## Not run: iai::load_graphviz()
Julia Equivalent:
IAI.MeanImputationLearner
mean_imputation_learner(...)mean_imputation_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::mean_imputation_learner()## Not run: lnr <- iai::mean_imputation_learner()
Julia Equivalent:
IAI.missing_goes_lower
missing_goes_lower(lnr, node_index)missing_goes_lower(lnr, node_index)
lnr |
The learner to query. |
node_index |
The node in the tree to query. |
## Not run: iai::missing_goes_lower(lnr, 1)## Not run: iai::missing_goes_lower(lnr, 1)
Generic function for constructing an interactive questionnaire with multiple learners
multi_questionnaire(obj, ...)multi_questionnaire(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Refer to the documentation on advanced tree visualization for more information.
## Default S3 method: multi_questionnaire(obj, ...)## Default S3 method: multi_questionnaire(obj, ...)
obj |
The questions to visualize. Refer to the Julia documentation on multi-learner visualizations for more information. |
... |
Additional arguments (unused) |
Julia Equivalent:
IAI.MultiQuestionnaire
Requires IAI version 1.1 or higher.
## Not run: iai::multi_questionnaire(list("Questionnaire for" = list( "first learner" = lnr1, "second learner" = lnr2 ))) ## End(Not run)## Not run: iai::multi_questionnaire(list("Questionnaire for" = list( "first learner" = lnr1, "second learner" = lnr2 ))) ## End(Not run)
Julia Equivalent:
IAI.MultiQuestionnaire
## S3 method for class 'grid_search' multi_questionnaire(obj, ...)## S3 method for class 'grid_search' multi_questionnaire(obj, ...)
obj |
The grid to visualize |
... |
Additional arguments (unused) |
Requires IAI version 2.0 or higher.
## Not run: iai::multi_questionnaire(grid)## Not run: iai::multi_questionnaire(grid)
Generic function for constructing an interactive tree visualization of multiple tree learners
multi_tree_plot(obj, ...)multi_tree_plot(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Refer to the documentation on advanced tree visualization for more information.
## Default S3 method: multi_tree_plot(obj, ...)## Default S3 method: multi_tree_plot(obj, ...)
obj |
The questions to visualize. Refer to the Julia documentation on multi-learner visualizations for more information. |
... |
Additional arguments (unused) |
Julia Equivalent:
IAI.MultiTreePlot
Requires IAI version 1.1 or higher.
## Not run: iai::multi_tree_plot(list("Visualizing" = list( "first learner" = lnr1, "second learner" = lnr2 ))) ## End(Not run)## Not run: iai::multi_tree_plot(list("Visualizing" = list( "first learner" = lnr1, "second learner" = lnr2 ))) ## End(Not run)
Julia Equivalent:
IAI.MultiTreePlot
## S3 method for class 'grid_search' multi_tree_plot(obj, ...)## S3 method for class 'grid_search' multi_tree_plot(obj, ...)
obj |
The grid to visualize |
... |
Additional arguments (unused) |
Requires IAI version 2.0 or higher.
## Not run: iai::multi_tree_plot(grid)## Not run: iai::multi_tree_plot(grid)
Julia Equivalent:
IAI.NumericClassificationRewardEstimator
numeric_classification_reward_estimator(...)numeric_classification_reward_estimator(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.2 or higher.
## Not run: lnr <- iai::numeric_classification_reward_estimator()## Not run: lnr <- iai::numeric_classification_reward_estimator()
Julia Equivalent:
IAI.NumericRegressionRewardEstimator
numeric_regression_reward_estimator(...)numeric_regression_reward_estimator(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.2 or higher.
## Not run: lnr <- iai::numeric_regression_reward_estimator()## Not run: lnr <- iai::numeric_regression_reward_estimator()
This function was deprecated in iai 1.6.0, and [numeric_classification_reward_estimator()] or [numeric_classification_reward_estimator()] should be used instead.
numeric_reward_estimator(...)numeric_reward_estimator(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
This deprecation is no longer supported as of the IAI v3 release.
Requires IAI version 2.1 or 2.2.
## Not run: lnr <- iai::numeric_reward_estimator()## Not run: lnr <- iai::numeric_reward_estimator()
Julia Equivalent:
IAI.NumericSurvivalRewardEstimator
numeric_survival_reward_estimator(...)numeric_survival_reward_estimator(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.2 or higher.
## Not run: lnr <- iai::numeric_survival_reward_estimator()## Not run: lnr <- iai::numeric_survival_reward_estimator()
Julia Equivalent:
IAI.OptKNNImputationLearner
opt_knn_imputation_learner(...)opt_knn_imputation_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::opt_knn_imputation_learner()## Not run: lnr <- iai::opt_knn_imputation_learner()
Julia Equivalent:
IAI.OptSVMImputationLearner
opt_svm_imputation_learner(...)opt_svm_imputation_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::opt_svm_imputation_learner()## Not run: lnr <- iai::opt_svm_imputation_learner()
Julia Equivalent:
IAI.OptTreeImputationLearner
opt_tree_imputation_learner(...)opt_tree_imputation_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::opt_tree_imputation_learner()## Not run: lnr <- iai::opt_tree_imputation_learner()
Julia Equivalent:
IAI.OptimalFeatureSelectionClassifier
optimal_feature_selection_classifier(...)optimal_feature_selection_classifier(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 1.1 or higher.
## Not run: lnr <- iai::optimal_feature_selection_classifier()## Not run: lnr <- iai::optimal_feature_selection_classifier()
Julia Equivalent:
IAI.OptimalFeatureSelectionRegressor
optimal_feature_selection_regressor(...)optimal_feature_selection_regressor(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 1.1 or higher.
## Not run: lnr <- iai::optimal_feature_selection_regressor()## Not run: lnr <- iai::optimal_feature_selection_regressor()
Julia Equivalent:
IAI.OptimalTreeClassifier
optimal_tree_classifier(...)optimal_tree_classifier(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::optimal_tree_classifier()## Not run: lnr <- iai::optimal_tree_classifier()
Julia Equivalent:
IAI.OptimalTreeMultiClassifier
optimal_tree_multi_classifier(...)optimal_tree_multi_classifier(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: lnr <- iai::optimal_tree_multi_classifier()## Not run: lnr <- iai::optimal_tree_multi_classifier()
Julia Equivalent:
IAI.OptimalTreeMultiRegressor
optimal_tree_multi_regressor(...)optimal_tree_multi_regressor(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: lnr <- iai::optimal_tree_multi_regressor()## Not run: lnr <- iai::optimal_tree_multi_regressor()
Julia Equivalent:
IAI.OptimalTreePolicyMaximizer
optimal_tree_policy_maximizer(...)optimal_tree_policy_maximizer(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.0 or higher.
## Not run: lnr <- iai::optimal_tree_policy_maximizer()## Not run: lnr <- iai::optimal_tree_policy_maximizer()
Julia Equivalent:
IAI.OptimalTreePolicyMinimizer
optimal_tree_policy_minimizer(...)optimal_tree_policy_minimizer(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.0 or higher.
## Not run: lnr <- iai::optimal_tree_policy_minimizer()## Not run: lnr <- iai::optimal_tree_policy_minimizer()
Julia Equivalent:
IAI.OptimalTreePrescriptionMaximizer
optimal_tree_prescription_maximizer(...)optimal_tree_prescription_maximizer(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::optimal_tree_prescription_maximizer()## Not run: lnr <- iai::optimal_tree_prescription_maximizer()
Julia Equivalent:
IAI.OptimalTreePrescriptionMinimizer
optimal_tree_prescription_minimizer(...)optimal_tree_prescription_minimizer(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::optimal_tree_prescription_minimizer()## Not run: lnr <- iai::optimal_tree_prescription_minimizer()
Julia Equivalent:
IAI.OptimalTreeRegressor
optimal_tree_regressor(...)optimal_tree_regressor(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::optimal_tree_regressor()## Not run: lnr <- iai::optimal_tree_regressor()
Julia Equivalent:
IAI.OptimalTreeSurvivalLearner
optimal_tree_survival_learner(...)optimal_tree_survival_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::optimal_tree_survival_learner()## Not run: lnr <- iai::optimal_tree_survival_learner()
This function was deprecated and renamed to optimal_tree_survival_learner()
in iai 1.3.0. This is for consistency with the IAI v2.0.0 Julia release.
optimal_tree_survivor(...)optimal_tree_survivor(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::optimal_tree_survivor()## Not run: lnr <- iai::optimal_tree_survivor()
Plot a grid search results for Optimal Feature Selection learners
## S3 method for class 'grid_search' plot(x, ...)## S3 method for class 'grid_search' plot(x, ...)
x |
The grid search to plot |
... |
Additional arguments (passed to
|
Requires IAI version 2.2 or higher.
## Not run: plot(grid)## Not run: plot(grid)
Plot an ROC curve
## S3 method for class 'roc_curve' plot(x, ...)## S3 method for class 'roc_curve' plot(x, ...)
x |
The ROC curve to plot |
... |
Additional arguments (unused) |
Requires IAI version 2.1 or higher.
## Not run: plot(roc)## Not run: plot(roc)
Plot a similarity comparison
## S3 method for class 'similarity_comparison' plot(x, ...)## S3 method for class 'similarity_comparison' plot(x, ...)
x |
The similarity comparison to plot |
... |
Additional arguments (unused) |
Requires IAI version 2.2 or higher.
## Not run: plot(similarity)## Not run: plot(similarity)
Plot a stability analysis
## S3 method for class 'stability_analysis' plot(x, ...)## S3 method for class 'stability_analysis' plot(x, ...)
x |
The stability analysis to plot |
... |
Additional arguments (unused) |
Requires IAI version 2.2 or higher.
## Not run: plot(stability)## Not run: plot(stability)
Generic function for returning the predictions of a model
predict(obj, ...)predict(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Generic function for returning the expected survival time predicted by a model
predict_expected_survival_time(obj, ...)predict_expected_survival_time(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
glmnetcv_survival_learner for each point in the features.Julia Equivalent:
IAI.predict_expected_survival_time
## S3 method for class 'glmnetcv_survival_learner' predict_expected_survival_time(obj, X, fit_index = NULL, ...)## S3 method for class 'glmnetcv_survival_learner' predict_expected_survival_time(obj, X, fit_index = NULL, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
fit_index |
The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied. |
... |
Additional arguments (unused) |
Requires IAI version 3.0 or higher.
## Not run: iai::predict_expected_survival_time(lnr, X)## Not run: iai::predict_expected_survival_time(lnr, X)
predict.survival_learner
or
get_survival_curve)Julia Equivalent:
IAI.predict_expected_survival_time
## S3 method for class 'survival_curve' predict_expected_survival_time(obj, ...)## S3 method for class 'survival_curve' predict_expected_survival_time(obj, ...)
obj |
The survival curve to use for prediction. |
... |
Additional arguments (unused) |
Requires IAI version 2.2 or higher.
## Not run: iai::predict_expected_survival_time(curve)## Not run: iai::predict_expected_survival_time(curve)
Julia Equivalent:
IAI.predict_expected_survival_time
## S3 method for class 'survival_learner' predict_expected_survival_time(obj, X, ...)## S3 method for class 'survival_learner' predict_expected_survival_time(obj, X, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
... |
Additional arguments (unused) |
Requires IAI version 2.0 or higher.
## Not run: iai::predict_expected_survival_time(lnr, X)## Not run: iai::predict_expected_survival_time(lnr, X)
Generic function for returning the hazard coefficient predicted by a model
predict_hazard(obj, ...)predict_hazard(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
glmnetcv_survival_learner for each point in the features.A higher hazard coefficient estimate corresponds to a smaller predicted survival time.
## S3 method for class 'glmnetcv_survival_learner' predict_hazard(obj, X, fit_index = NULL, ...)## S3 method for class 'glmnetcv_survival_learner' predict_hazard(obj, X, fit_index = NULL, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
fit_index |
The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied. |
... |
Additional arguments (unused) |
Julia Equivalent:
IAI.predict_hazard
Requires IAI version 3.0 or higher.
## Not run: iai::predict_hazard(lnr, X)## Not run: iai::predict_hazard(lnr, X)
A higher hazard coefficient estimate corresponds to a smaller predicted survival time.
## S3 method for class 'survival_learner' predict_hazard(obj, X, ...)## S3 method for class 'survival_learner' predict_hazard(obj, X, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
... |
Additional arguments (unused) |
Julia Equivalent:
IAI.predict_hazard
Requires IAI version 1.2 or higher.
## Not run: iai::predict_hazard(lnr, X)## Not run: iai::predict_hazard(lnr, X)
Generic function for returning the outcomes predicted by a model under each treatment
predict_outcomes(obj, ...)predict_outcomes(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.predict_outcomes
## S3 method for class 'policy_learner' predict_outcomes(obj, X, rewards, ...)## S3 method for class 'policy_learner' predict_outcomes(obj, X, rewards, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
rewards |
The estimated reward matrix for the data. |
... |
Additional arguments (unused) |
Requires IAI version 2.0 or higher
## Not run: iai::predict_outcomes(lnr, X, rewards)## Not run: iai::predict_outcomes(lnr, X, rewards)
Julia Equivalent:
IAI.predict_outcomes
## S3 method for class 'prescription_learner' predict_outcomes(obj, X, ...)## S3 method for class 'prescription_learner' predict_outcomes(obj, X, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
... |
Additional arguments (unused) |
## Not run: iai::predict_outcomes(lnr, X)## Not run: iai::predict_outcomes(lnr, X)
Generic function for returning the probabilities of class membership predicted by a model
predict_proba(obj, ...)predict_proba(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.predict_proba
## S3 method for class 'classification_learner' predict_proba(obj, X, ...)## S3 method for class 'classification_learner' predict_proba(obj, X, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
... |
Additional arguments (unused) |
## Not run: iai::predict_proba(lnr, X)## Not run: iai::predict_proba(lnr, X)
Julia Equivalent:
IAI.predict_proba
and
IAI.predict_proba
## S3 method for class 'classification_multi_learner' predict_proba(obj, X, ...)## S3 method for class 'classification_multi_learner' predict_proba(obj, X, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
... |
Additional arguments (unused) |
Requires IAI version 3.2 or higher.
## Not run: iai::predict_proba(lnr, X)## Not run: iai::predict_proba(lnr, X)
glmnetcv_classifier learner for each point in the featuresJulia Equivalent:
IAI.predict_proba
## S3 method for class 'glmnetcv_classifier' predict_proba(obj, X, fit_index = NULL, ...)## S3 method for class 'glmnetcv_classifier' predict_proba(obj, X, fit_index = NULL, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
fit_index |
The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied. |
... |
Additional arguments (unused) |
Requires IAI version 3.0 or higher.
## Not run: iai::predict_proba(lnr, X)## Not run: iai::predict_proba(lnr, X)
Generic function for returning the counterfactual rewards estimated by a model under each treatment
predict_reward(obj, ...)predict_reward(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.predict_reward
## S3 method for class 'categorical_reward_estimator' predict_reward(obj, X, ...)## S3 method for class 'categorical_reward_estimator' predict_reward(obj, X, ...)
obj |
The learner or grid to use for estimation |
X |
The features of the data. |
... |
Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information. |
Requires IAI version 3.0 or higher.
## Not run: iai::predict_reward(lnr, X, treatments, outcomes, predictions)## Not run: iai::predict_reward(lnr, X, treatments, outcomes, predictions)
Julia Equivalent:
IAI.predict_reward
## S3 method for class 'numeric_reward_estimator' predict_reward(obj, X, ...)## S3 method for class 'numeric_reward_estimator' predict_reward(obj, X, ...)
obj |
The learner or grid to use for estimation |
X |
The features of the data. |
... |
Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information. |
Requires IAI version 3.0 or higher.
## Not run: iai::predict_reward(lnr, X, treatments, outcomes, predictions)## Not run: iai::predict_reward(lnr, X, treatments, outcomes, predictions)
Julia Equivalent:
IAI.predict_shap
predict_shap(lnr, X)predict_shap(lnr, X)
lnr |
The XGBoost learner or grid to use for prediction. |
X |
The features of the data. |
Requires IAI version 2.2 or higher.
## Not run: iai::predict_shap(lnr, X)## Not run: iai::predict_shap(lnr, X)
Julia Equivalent:
IAI.predict_treatment_outcome
predict_treatment_outcome(lnr, X)predict_treatment_outcome(lnr, X)
lnr |
The learner or grid to use for prediction. |
X |
The features of the data. |
Requires IAI version 2.1 or higher.
## Not run: iai::predict_treatment_outcome(lnr, X)## Not run: iai::predict_treatment_outcome(lnr, X)
Julia Equivalent:
IAI.predict_treatment_outcome_standard_error
predict_treatment_outcome_standard_error(lnr, X)predict_treatment_outcome_standard_error(lnr, X)
lnr |
The learner or grid to use for prediction. |
X |
The features of the data. |
Requires IAI version 3.2 or higher.
## Not run: iai::predict_treatment_outcome_standard_error(lnr, X)## Not run: iai::predict_treatment_outcome_standard_error(lnr, X)
Julia Equivalent:
IAI.predict_treatment_rank
predict_treatment_rank(lnr, X)predict_treatment_rank(lnr, X)
lnr |
The learner or grid to use for prediction. |
X |
The features of the data. |
Requires IAI version 2.1 or higher.
## Not run: iai::predict_treatment_rank(lnr, X)## Not run: iai::predict_treatment_rank(lnr, X)
Julia Equivalent:
IAI.predict
## S3 method for class 'categorical_reward_estimator' predict(obj, X, ...)## S3 method for class 'categorical_reward_estimator' predict(obj, X, ...)
obj |
The learner or grid to use for estimation |
X |
The features of the data. |
... |
Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information. |
Requires IAI version 2.0 or higher.
## Not run: iai::predict(lnr, X, treatments, outcomes)## Not run: iai::predict(lnr, X, treatments, outcomes)
Julia Equivalent:
IAI.predict
## S3 method for class 'glmnetcv_learner' predict(obj, X, fit_index = NULL, ...)## S3 method for class 'glmnetcv_learner' predict(obj, X, fit_index = NULL, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
fit_index |
The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: iai::predict(lnr, X)## Not run: iai::predict(lnr, X)
Julia Equivalent:
IAI.predict
## S3 method for class 'numeric_reward_estimator' predict(obj, X, ...)## S3 method for class 'numeric_reward_estimator' predict(obj, X, ...)
obj |
The learner or grid to use for estimation |
X |
The features of the data. |
... |
Additional arguments depending on the treatment and outcome types. Refer to the Julia documentation for more information. |
Requires IAI version 2.1 or higher.
## Not run: iai::predict(lnr, X, treatments, outcomes)## Not run: iai::predict(lnr, X, treatments, outcomes)
Julia Equivalent:
IAI.predict
## S3 method for class 'optimal_feature_selection_learner' predict(obj, X, fit_index = NULL, ...)## S3 method for class 'optimal_feature_selection_learner' predict(obj, X, fit_index = NULL, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
fit_index |
The index of the cluster to use for prediction, if the
|
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 1.1 or higher.
## Not run: iai::predict(lnr, X)## Not run: iai::predict(lnr, X)
Julia Equivalent:
IAI.predict
## S3 method for class 'supervised_learner' predict(obj, X, ...)## S3 method for class 'supervised_learner' predict(obj, X, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::predict(lnr, X)## Not run: iai::predict(lnr, X)
Julia Equivalent:
IAI.predict
and
IAI.predict
## S3 method for class 'supervised_multi_learner' predict(obj, X, ...)## S3 method for class 'supervised_multi_learner' predict(obj, X, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: iai::predict(lnr, X)## Not run: iai::predict(lnr, X)
Julia Equivalent:
IAI.predict
## S3 method for class 'survival_learner' predict(obj, X, t = NULL, ...)## S3 method for class 'survival_learner' predict(obj, X, t = NULL, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
t |
The time for which to predict survival probability, defaulting to returning the entire survival curve if not supplied |
... |
Additional arguments (unused) |
## Not run: iai::predict(lnr, X, t = 10)## Not run: iai::predict(lnr, X, t = 10)
Julia Equivalent:
IAI.print_path
print_path(lnr, X, ...)print_path(lnr, X, ...)
lnr |
The learner or grid to query. |
X |
The features of the data. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::print_path(lnr, X) iai::print_path(lnr, X, 1) ## End(Not run)## Not run: iai::print_path(lnr, X) iai::print_path(lnr, X, 1) ## End(Not run)
Julia Equivalent:
IAI.prune_trees!
prune_trees(lnr, ...)prune_trees(lnr, ...)
lnr |
The learner to prune |
... |
Refer to the Julia documentation for available parameters |
Requires IAI version 3.0 or higher.
## Not run: iai::prune_trees(lnr, ...)## Not run: iai::prune_trees(lnr, ...)
Julia Equivalent:
IAI.Questionnaire
questionnaire(obj, ...)questionnaire(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.Questionnaire
## S3 method for class 'optimal_feature_selection_learner' questionnaire(obj, ...)## S3 method for class 'optimal_feature_selection_learner' questionnaire(obj, ...)
obj |
The learner to visualize. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: iai::questionnaire(lnr)## Not run: iai::questionnaire(lnr)
Julia Equivalent:
IAI.Questionnaire
## S3 method for class 'tree_learner' questionnaire(obj, ...)## S3 method for class 'tree_learner' questionnaire(obj, ...)
obj |
The learner to visualize. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 1.1 or higher.
## Not run: iai::questionnaire(lnr)## Not run: iai::questionnaire(lnr)
Julia Equivalent:
IAI.RandImputationLearner
rand_imputation_learner(...)rand_imputation_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::rand_imputation_learner()## Not run: lnr <- iai::rand_imputation_learner()
Julia Equivalent:
IAI.RandomForestClassifier
random_forest_classifier(...)random_forest_classifier(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: lnr <- iai::random_forest_classifier()## Not run: lnr <- iai::random_forest_classifier()
Julia Equivalent:
IAI.RandomForestRegressor
random_forest_regressor(...)random_forest_regressor(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: lnr <- iai::random_forest_regressor()## Not run: lnr <- iai::random_forest_regressor()
Julia Equivalent:
IAI.RandomForestSurvivalLearner
random_forest_survival_learner(...)random_forest_survival_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.2 or higher.
## Not run: lnr <- iai::random_forest_survival_learner()## Not run: lnr <- iai::random_forest_survival_learner()
Julia Equivalent:
IAI.read_json
read_json(filename)read_json(filename)
filename |
The location of the JSON file. |
## Not run: obj <- iai::read_json("out.json")## Not run: obj <- iai::read_json("out.json")
Julia Equivalent:
IAI.refit_leaves!
refit_leaves(lnr, ...)refit_leaves(lnr, ...)
lnr |
The learner to refit |
... |
Refer to the Julia documentation for available parameters |
Requires IAI version 3.0 or higher.
## Not run: iai::refit_leaves(lnr, ...)## Not run: iai::refit_leaves(lnr, ...)
Julia Equivalent:
IAI.release_license
release_license()release_license()
Requires IAI version 3.1 or higher.
## Not run: iai::release_license()## Not run: iai::release_license()
Julia Equivalent:
IAI.reset_display_label!
reset_display_label(lnr)reset_display_label(lnr)
lnr |
The learner to modify. |
## Not run: iai::reset_display_label(lnr)## Not run: iai::reset_display_label(lnr)
Julia Equivalent:
IAI.resume_from_checkpoint
resume_from_checkpoint(checkpoint_file)resume_from_checkpoint(checkpoint_file)
checkpoint_file |
The location of the checkpoint file. |
Requires IAI version 3.1 or higher.
## Not run: obj <- iai::resume_from_checkpoint("checkpoint.json")## Not run: obj <- iai::resume_from_checkpoint("checkpoint.json")
This function was deprecated and renamed to categorical_reward_estimator()
in iai 1.4.0. This is for consistency with the IAI v2.1.0 Julia release.
reward_estimator(...)reward_estimator(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
This deprecation is no longer supported as of the IAI v3 release.
Requires IAI version 2.2 or lower.
## Not run: lnr <- iai::reward_estimator()## Not run: lnr <- iai::reward_estimator()
Julia Equivalent:
IAI.ROCCurve
roc_curve(obj, ...)roc_curve(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.ROCCurve
## S3 method for class 'classification_learner' roc_curve(obj, X, y, ...)## S3 method for class 'classification_learner' roc_curve(obj, X, y, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
y |
The labels of the data. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::roc_curve(lnr, X, y)## Not run: iai::roc_curve(lnr, X, y)
Julia Equivalent:
IAI.ROCCurve
and
IAI.ROCCurve
## S3 method for class 'classification_multi_learner' roc_curve(obj, X, y, ...)## S3 method for class 'classification_multi_learner' roc_curve(obj, X, y, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
y |
The labels of the data. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: iai::roc_curve(lnr, X, y)## Not run: iai::roc_curve(lnr, X, y)
Julia Equivalent:
IAI.ROCCurve
## Default S3 method: roc_curve(obj, y, positive_label = stop("`positive_label` is required"), ...)## Default S3 method: roc_curve(obj, y, positive_label = stop("`positive_label` is required"), ...)
obj |
The predicted probabilities for each point in the data. |
y |
The true labels of the data. |
positive_label |
The label for which probability is being predicted. |
... |
Additional arguments (unused) |
Requires IAI version 2.0 or higher.
## Not run: iai::roc_curve(probs, y, positive_label=positive_label)## Not run: iai::roc_curve(probs, y, positive_label=positive_label)
glmnetcv_classifier
on the given dataJulia Equivalent:
IAI.ROCCurve
## S3 method for class 'glmnetcv_classifier' roc_curve(obj, X, y, fit_index = NULL, ...)## S3 method for class 'glmnetcv_classifier' roc_curve(obj, X, y, fit_index = NULL, ...)
obj |
The learner or grid to use for prediction. |
X |
The features of the data. |
y |
The labels of the data. |
fit_index |
The index of the fit in the path to use for prediction, defaulting to the best fit if not supplied. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.0 or higher.
## Not run: iai::roc_curve(lnr, X, y)## Not run: iai::roc_curve(lnr, X, y)
Generic function for calculating scores
score(obj, ...)score(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.score
## S3 method for class 'categorical_reward_estimator' score(obj, X, ...)## S3 method for class 'categorical_reward_estimator' score(obj, X, ...)
obj |
The learner or grid to evaluate. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for other available parameters. |
Requires IAI version 2.1 or higher.
## Not run: iai::score(lnr, X, treatments, outcomes)## Not run: iai::score(lnr, X, treatments, outcomes)
Julia Equivalent:
IAI.score
## Default S3 method: score(obj, predictions, truths, ...)## Default S3 method: score(obj, predictions, truths, ...)
obj |
The type of problem. |
predictions |
The predictions to evaluate. |
truths |
The true target values for these observations. |
... |
Other parameters, including the criterion. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: iai::score("regression", y_pred, y_true, criterion="mse")## Not run: iai::score("regression", y_pred, y_true, criterion="mse")
Julia Equivalent:
IAI.score
## S3 method for class 'glmnetcv_learner' score(obj, X, ...)## S3 method for class 'glmnetcv_learner' score(obj, X, ...)
obj |
The learner or grid to evaluate. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as
required by the problem type. |
Requires IAI version 2.1 or higher.
## Not run: iai::score(lnr, X, y, fit_index=1)## Not run: iai::score(lnr, X, y, fit_index=1)
Julia Equivalent:
IAI.score
## S3 method for class 'numeric_reward_estimator' score(obj, X, ...)## S3 method for class 'numeric_reward_estimator' score(obj, X, ...)
obj |
The learner or grid to evaluate. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for other available parameters. |
Requires IAI version 2.1 or higher.
## Not run: iai::score(lnr, X, treatments, outcomes)## Not run: iai::score(lnr, X, treatments, outcomes)
Julia Equivalent:
IAI.score
## S3 method for class 'optimal_feature_selection_learner' score(obj, X, ...)## S3 method for class 'optimal_feature_selection_learner' score(obj, X, ...)
obj |
The learner or grid to evaluate. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as
required by the problem type. If the |
Requires IAI version 1.1 or higher.
## Not run: iai::score(lnr, X, y, fit_index=1)## Not run: iai::score(lnr, X, y, fit_index=1)
Julia Equivalent:
IAI.score
## S3 method for class 'supervised_learner' score(obj, X, ...)## S3 method for class 'supervised_learner' score(obj, X, ...)
obj |
The learner or grid to evaluate. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters. |
## Not run: iai::score(lnr, X, y)## Not run: iai::score(lnr, X, y)
Julia Equivalent:
IAI.score
and
IAI.score
## S3 method for class 'supervised_multi_learner' score(obj, X, ...)## S3 method for class 'supervised_multi_learner' score(obj, X, ...)
obj |
The learner or grid to evaluate. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters. |
Requires IAI version 3.2 or higher.
## Not run: iai::score(lnr, X, y)## Not run: iai::score(lnr, X, y)
Julia Equivalent:
IAI.set_display_label!
set_display_label(lnr, display_label)set_display_label(lnr, display_label)
lnr |
The learner to modify. |
display_label |
The label for which to show probabilities. |
## Not run: iai::set_display_label(lnr, "A")## Not run: iai::set_display_label(lnr, "A")
Julia Equivalent:
Random.seed!
set_julia_seed(seed)set_julia_seed(seed)
seed |
The seed to set |
## Not run: iai::set_julia_seed(1)## Not run: iai::set_julia_seed(1)
Julia Equivalent:
IAI.set_params!
set_params(lnr, ...)set_params(lnr, ...)
lnr |
The learner to modify. |
... |
The parameters to set on the learner. |
## Not run: iai::set_params(lnr, random_seed = 1)## Not run: iai::set_params(lnr, random_seed = 1)
Julia Equivalent:
IAI.set_reward_kernel_bandwidth!
set_reward_kernel_bandwidth(lnr, ...)set_reward_kernel_bandwidth(lnr, ...)
lnr |
The learner to modify |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 2.2 or higher.
## Not run: iai::set_reward_kernel_bandwidth(lnr, ...)## Not run: iai::set_reward_kernel_bandwidth(lnr, ...)
Julia Equivalent:
IAI.set_rich_output_param!
set_rich_output_param(key, value)set_rich_output_param(key, value)
key |
The parameter to set. |
value |
The value to set |
## Not run: iai::set_rich_output_param("simple_layout", TRUE)## Not run: iai::set_rich_output_param("simple_layout", TRUE)
Julia Equivalent:
IAI.set_threshold!
set_threshold(lnr, label, threshold, ...)set_threshold(lnr, label, threshold, ...)
lnr |
The learner to modify. |
label |
The referenced label. |
threshold |
The probability threshold above which |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::set_threshold(lnr, "A", 0.4)## Not run: iai::set_threshold(lnr, "A", 0.4)
Generic function for showing interactive visualization in browser
show_in_browser(obj, ...)show_in_browser(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.show_in_browser
## S3 method for class 'abstract_visualization' show_in_browser(obj, ...)## S3 method for class 'abstract_visualization' show_in_browser(obj, ...)
obj |
The object to visualize. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::show_in_browser(lnr)## Not run: iai::show_in_browser(lnr)
roc_curve in the default
browserJulia Equivalent:
IAI.show_in_browser
## S3 method for class 'roc_curve' show_in_browser(obj, ...)## S3 method for class 'roc_curve' show_in_browser(obj, ...)
obj |
The curve to visualize. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 1.1 or higher.
## Not run: iai::show_in_browser(curve)## Not run: iai::show_in_browser(curve)
Julia Equivalent:
IAI.show_in_browser
## S3 method for class 'tree_learner' show_in_browser(obj, ...)## S3 method for class 'tree_learner' show_in_browser(obj, ...)
obj |
The learner or grid to visualize. |
... |
Refer to the Julia documentation for available parameters. |
Showing a grid search requires IAI version 2.0 or higher.
## Not run: iai::show_in_browser(lnr)## Not run: iai::show_in_browser(lnr)
Generic function for showing interactive questionnaire in browser
show_questionnaire(obj, ...)show_questionnaire(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.show_questionnaire
## S3 method for class 'optimal_feature_selection_learner' show_questionnaire(obj, ...)## S3 method for class 'optimal_feature_selection_learner' show_questionnaire(obj, ...)
obj |
The learner or grid to visualize. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: iai::show_questionnaire(lnr)## Not run: iai::show_questionnaire(lnr)
Julia Equivalent:
IAI.show_questionnaire
## S3 method for class 'tree_learner' show_questionnaire(obj, ...)## S3 method for class 'tree_learner' show_questionnaire(obj, ...)
obj |
The learner or grid to visualize. |
... |
Refer to the Julia documentation for available parameters. |
Showing a grid search requires IAI version 2.0 or higher.
## Not run: iai::show_questionnaire(lnr)## Not run: iai::show_questionnaire(lnr)
Refer to the documentation on tree stability for more information.
similarity_comparison(lnr, new_lnr, deviations)similarity_comparison(lnr, new_lnr, deviations)
lnr |
The original learner |
new_lnr |
The new learner |
deviations |
The deviation between the original tree and each tree in the new learner |
Julia Equivalent:
IAI.SimilarityComparison
Requires IAI version 2.2 or higher.
## Not run: iai::similarity_comparison(lnr, new_lnr, deviations)## Not run: iai::similarity_comparison(lnr, new_lnr, deviations)
Julia Equivalent:
IAI.SingleKNNImputationLearner
single_knn_imputation_learner(...)single_knn_imputation_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
## Not run: lnr <- iai::single_knn_imputation_learner()## Not run: lnr <- iai::single_knn_imputation_learner()
Julia Equivalent:
IAI.split_data
split_data(task, X, ...)split_data(task, X, ...)
task |
The type of problem. |
X |
The features of the data. |
... |
Other parameters, including zero or more target vectors as required by the problem type. Refer to the Julia documentation for available parameters. |
## Not run: X <- iris[, 1:4] y <- iris$Species split <- iai::split_data("classification", X, y, train_proportion = 0.75) train_X <- split$train$X train_y <- split$train$y test_X <- split$test$X test_y <- split$test$y ## End(Not run)## Not run: X <- iris[, 1:4] y <- iris$Species split <- iai::split_data("classification", X, y, train_proportion = 0.75) train_X <- split$train$X train_y <- split$train$y test_X <- split$test$X test_y <- split$test$y ## End(Not run)
Refer to the documentation on tree stability for more information.
stability_analysis(lnr, ...)stability_analysis(lnr, ...)
lnr |
The original learner |
... |
Additional arguments (refer to Julia documentation) |
Julia Equivalent:
IAI.StabilityAnalysis
Requires IAI version 2.2 or higher.
## Not run: iai::stability_analysis(lnr, ...)## Not run: iai::stability_analysis(lnr, ...)
Julia Equivalent:
IAI.transform
transform(lnr, X)transform(lnr, X)
lnr |
The learner or grid to use for imputation |
X |
The features of the data. |
## Not run: iai::transform(lnr, X)## Not run: iai::transform(lnr, X)
Julia Equivalent:
IAI.transform_and_expand
transform_and_expand(lnr, X, ...)transform_and_expand(lnr, X, ...)
lnr |
The learner to use for imputation. |
X |
The dataframe in which to impute missing values. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 3.0 or higher.
## Not run: lnr <- iai::transform_and_expand(lnr, X, type = "finite")## Not run: lnr <- iai::transform_and_expand(lnr, X, type = "finite")
Julia Equivalent:
IAI.TreePlot
tree_plot(lnr, ...)tree_plot(lnr, ...)
lnr |
The learner to visualize. |
... |
Refer to the Julia documentation on advanced tree visualization for available parameters. |
Requires IAI version 1.1 or higher.
## Not run: iai::tree_plot(lnr)## Not run: iai::tree_plot(lnr)
Julia Equivalent:
IAI.tune_reward_kernel_bandwidth
tune_reward_kernel_bandwidth(lnr, ...)tune_reward_kernel_bandwidth(lnr, ...)
lnr |
The learner to use for tuning the bandwidth |
... |
Refer to the Julia documentation for other parameters |
Requires IAI version 2.2 or higher.
## Not run: iai::tune_reward_kernel_bandwidth(lnr, ...)## Not run: iai::tune_reward_kernel_bandwidth(lnr, ...)
Generic function for calculating variable importance
variable_importance(obj, ...)variable_importance(obj, ...)
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.variable_importance_similarity
variable_importance_similarity(lnr, new_lnr, ...)variable_importance_similarity(lnr, new_lnr, ...)
lnr |
The original learner |
new_lnr |
The new learner |
... |
Additional arguments (refer to Julia documentation) |
Requires IAI version 2.2 or higher.
## Not run: iai::variable_importance_similarity(lnr, new_lnr)## Not run: iai::variable_importance_similarity(lnr, new_lnr)
Julia Equivalent:
IAI.variable_importance
## S3 method for class 'learner' variable_importance(obj, ...)## S3 method for class 'learner' variable_importance(obj, ...)
obj |
The learner to query. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::variable_importance(lnr, ...)## Not run: iai::variable_importance(lnr, ...)
Julia Equivalent:
IAI.variable_importance
## S3 method for class 'optimal_feature_selection_learner' variable_importance(obj, fit_index = NULL, ...)## S3 method for class 'optimal_feature_selection_learner' variable_importance(obj, fit_index = NULL, ...)
obj |
The learner to query. |
fit_index |
The index of the cluster to use for prediction, if the
|
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 1.1 or higher.
## Not run: iai::variable_importance(lnr, ...)## Not run: iai::variable_importance(lnr, ...)
Julia Equivalent:
IAI.variable_importance
## S3 method for class 'tree_learner' variable_importance(obj, ...)## S3 method for class 'tree_learner' variable_importance(obj, ...)
obj |
The learner to query. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::variable_importance(lnr, ...)## Not run: iai::variable_importance(lnr, ...)
Julia Equivalent:
IAI.write_booster
write_booster(filename, lnr)write_booster(filename, lnr)
filename |
Where to save the output. |
lnr |
The XGBoost learner with the booster to output. |
Requires IAI version 2.1 or higher.
## Not run: iai::write_booster(file.path(tempdir(), "out.json"), lnr)## Not run: iai::write_booster(file.path(tempdir(), "out.json"), lnr)
Julia Equivalent:
IAI.write_dot
write_dot(filename, lnr, ...)write_dot(filename, lnr, ...)
filename |
Where to save the output. |
lnr |
The learner to output. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::write_dot(file.path(tempdir(), "tree.dot"), lnr)## Not run: iai::write_dot(file.path(tempdir(), "tree.dot"), lnr)
Generic function for writing interactive visualization to file
write_html(filename, obj, ...)write_html(filename, obj, ...)
filename |
Where to save the output. |
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.write_html
## S3 method for class 'abstract_visualization' write_html(filename, obj, ...)## S3 method for class 'abstract_visualization' write_html(filename, obj, ...)
filename |
Where to save the output. |
obj |
The object to output. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::write_html(file.path(tempdir(), "out.html"), lnr)## Not run: iai::write_html(file.path(tempdir(), "out.html"), lnr)
Julia Equivalent:
IAI.write_html
## S3 method for class 'roc_curve' write_html(filename, obj, ...)## S3 method for class 'roc_curve' write_html(filename, obj, ...)
filename |
Where to save the output. |
obj |
The curve to output. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 1.1 or higher.
## Not run: iai::write_html(file.path(tempdir(), "roc.html"), lnr)## Not run: iai::write_html(file.path(tempdir(), "roc.html"), lnr)
Julia Equivalent:
IAI.write_html
## S3 method for class 'tree_learner' write_html(filename, obj, ...)## S3 method for class 'tree_learner' write_html(filename, obj, ...)
filename |
Where to save the output. |
obj |
The learner or grid to output. |
... |
Refer to the Julia documentation for available parameters. |
Outputting a grid search requires IAI version 2.0 or higher.
## Not run: iai::write_html(file.path(tempdir(), "tree.html"), lnr)## Not run: iai::write_html(file.path(tempdir(), "tree.html"), lnr)
Julia Equivalent:
IAI.write_json
write_json(filename, obj, ...)write_json(filename, obj, ...)
filename |
Where to save the output. |
obj |
The learner or grid to output. |
... |
Refer to the Julia documentation for available parameters. |
## Not run: iai::write_json(file.path(tempdir(), "out.json"), obj)## Not run: iai::write_json(file.path(tempdir(), "out.json"), obj)
Before using this function, either run load_graphviz or ensure
that Graphviz is installed and on the system
PATH
write_pdf(filename, lnr, ...)write_pdf(filename, lnr, ...)
filename |
Where to save the output. |
lnr |
The learner to output. |
... |
Refer to the Julia documentation for available parameters. |
Julia Equivalent:
IAI.write_pdf
Requires IAI version 2.1 or higher.
## Not run: iai::write_pdf(file.path(tempdir(), "tree.pdf"), lnr)## Not run: iai::write_pdf(file.path(tempdir(), "tree.pdf"), lnr)
Before using this function, either run load_graphviz or ensure
that Graphviz is installed and on the system
PATH
write_png(filename, lnr, ...)write_png(filename, lnr, ...)
filename |
Where to save the output. |
lnr |
The learner to output. |
... |
Refer to the Julia documentation for available parameters. |
Julia Equivalent:
IAI.write_png
## Not run: iai::write_png(file.path(tempdir(), "tree.png"), lnr)## Not run: iai::write_png(file.path(tempdir(), "tree.png"), lnr)
Generic function for writing interactive questionnaire to file
write_questionnaire(filename, obj, ...)write_questionnaire(filename, obj, ...)
filename |
Where to save the output. |
obj |
The object controlling which method is used |
... |
Arguments depending on the specific method used |
Julia Equivalent:
IAI.write_questionnaire
## S3 method for class 'optimal_feature_selection_learner' write_questionnaire(filename, obj, ...)## S3 method for class 'optimal_feature_selection_learner' write_questionnaire(filename, obj, ...)
filename |
Where to save the output. |
obj |
The learner or grid to output. |
... |
Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: iai::write_questionnaire(file.path(tempdir(), "questionnaire.html"), lnr)## Not run: iai::write_questionnaire(file.path(tempdir(), "questionnaire.html"), lnr)
Julia Equivalent:
IAI.write_questionnaire
## S3 method for class 'tree_learner' write_questionnaire(filename, obj, ...)## S3 method for class 'tree_learner' write_questionnaire(filename, obj, ...)
filename |
Where to save the output. |
obj |
The learner or grid to output. |
... |
Refer to the Julia documentation for available parameters. |
Outputting a grid search requires IAI version 2.0 or higher.
## Not run: iai::write_questionnaire(file.path(tempdir(), "questionnaire.html"), lnr)## Not run: iai::write_questionnaire(file.path(tempdir(), "questionnaire.html"), lnr)
Before using this function, either run load_graphviz or ensure
that Graphviz is installed and on the system
PATH
write_svg(filename, lnr, ...)write_svg(filename, lnr, ...)
filename |
Where to save the output. |
lnr |
The learner to output. |
... |
Refer to the Julia documentation for available parameters. |
Julia Equivalent:
IAI.write_svg
Requires IAI version 2.1 or higher.
## Not run: iai::write_svg(file.path(tempdir(), "tree.svg"), lnr)## Not run: iai::write_svg(file.path(tempdir(), "tree.svg"), lnr)
Julia Equivalent:
IAI.XGBoostClassifier
xgboost_classifier(...)xgboost_classifier(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: lnr <- iai::xgboost_classifier()## Not run: lnr <- iai::xgboost_classifier()
Julia Equivalent:
IAI.XGBoostRegressor
xgboost_regressor(...)xgboost_regressor(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.1 or higher.
## Not run: lnr <- iai::xgboost_regressor()## Not run: lnr <- iai::xgboost_regressor()
Julia Equivalent:
IAI.XGBoostSurvivalLearner
xgboost_survival_learner(...)xgboost_survival_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 2.2 or higher.
## Not run: lnr <- iai::xgboost_survival_learner()## Not run: lnr <- iai::xgboost_survival_learner()
Julia Equivalent:
IAI.ZeroImputationLearner
zero_imputation_learner(...)zero_imputation_learner(...)
... |
Use keyword arguments to set parameters on the resulting learner. Refer to the Julia documentation for available parameters. |
Requires IAI version 3.0 or higher.
## Not run: lnr <- iai::zero_imputation_learner()## Not run: lnr <- iai::zero_imputation_learner()