Package 'fastml'

Evaluate Models Function

Description

Evaluates the trained models on the test data and computes performance metrics.

Usage

evaluate_models(models, test_data, label, metric = "Accuracy")

Arguments

models	A list of trained model objects.
test_data	Preprocessed test data frame.
label	Name of the target variable.
metric	The performance metric to optimize (e.g., "Accuracy", "ROC").

Value

A list of performance metrics for each model.

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Fast Machine Learning Function

Description

Trains and evaluates multiple classification models.

Usage

fastml(
  data,
  label,
  algorithms = c("xgboost", "random_forest", "svm_radial"),
  test_size = 0.2,
  resampling_method = "cv",
  folds = 5,
  tune_params = NULL,
  metric = "Accuracy",
  n_cores = 1,
  stratify = TRUE,
  impute_method = NULL,
  encode_categoricals = TRUE,
  scaling_methods = c("center", "scale"),
  summaryFunction = NULL,
  seed = 123
)

Arguments

data	A data frame containing the features and target variable.
label	A string specifying the name of the target variable.
algorithms	A vector of algorithm names to use. Default is c("xgboost", "random_forest", "svm_radial"). Use "all" to run all supported algorithms.
test_size	A numeric value between 0 and 1 indicating the proportion of the data to use for testing. Default is 0.2.
resampling_method	A string specifying the resampling method for cross-validation. Default is "cv" (cross-validation). Other options include "none", "boot", "repeatedcv", etc.
folds	An integer specifying the number of folds for cross-validation. Default is 5.
tune_params	A list specifying hyperparameter tuning ranges. Default is NULL.
metric	The performance metric to optimize during training. Default is "Accuracy".
n_cores	An integer specifying the number of CPU cores to use for parallel processing. Default is 1.
stratify	Logical indicating whether to use stratified sampling when splitting the data. Default is TRUE.
impute_method	Method for missing value imputation. Default is NULL.
encode_categoricals	Logical indicating whether to encode categorical variables. Default is TRUE.
scaling_methods	Vector of scaling methods to apply. Default is c("center", "scale").
summaryFunction	A custom summary function for model evaluation. Default is NULL.
seed	An integer value specifying the random seed for reproducibility.

Value

An object of class fastml_model containing the best model, performance metrics, and other information.

Examples

# Example 1: Using the iris dataset for binary classification (excluding 'setosa')
data(iris)
iris <- iris[iris$Species != "setosa", ]  # Binary classification
iris$Species <- factor(iris$Species)

# Train models
model <- fastml(
  data = iris,
  label = "Species"
)

# View model summary
summary(model)


# Example 2: Using the mtcars dataset for binary classification
data(mtcars)
mtcars$am <- factor(mtcars$am)  # Convert transmission (0 = automatic, 1 = manual) to a factor

# Train models with a different resampling method and specific algorithms
model2 <- fastml(
  data = mtcars,
  label = "am",
  algorithms = c("random_forest", "svm_radial"),
  resampling_method = "repeatedcv",
  folds = 3,
  test_size = 0.25
)

# View model performance
summary(model2)


# Example 3: Using the airquality dataset with missing values
data(airquality)
airquality <- na.omit(airquality)  # Simple example to remove missing values for demonstration
airquality$Month <- factor(airquality$Month)

# Train models with categorical encoding and scaling
model3 <- fastml(
  data = airquality,
  label = "Month",
  encode_categoricals = TRUE,
  scaling_methods = c("center", "scale")
)

# Evaluate and compare models
summary(model3)


# Example 4: Custom hyperparameter tuning for a random forest
data(iris)
iris <- iris[iris$Species != "setosa", ]  # Filter out 'setosa' for binary classification
iris$Species <- factor(iris$Species)
custom_tuning <- list(
  random_forest = expand.grid(mtry = c(1:10))
)

model4 <- fastml(
  data = iris,
  label = "Species",
  algorithms = c("random_forest"),
  tune_params = custom_tuning,
  metric = "Accuracy"
)

# View the results
summary(model4)

---

Load Model Function

Description

Loads a trained model object from a file.

Usage

load_model(filepath)

Arguments

filepath

A string specifying the file path to load the model from.

Value

An object of class fastml_model.

---

Plot Function for fastml_model

Description

Generates plots to compare the performance of different models.

Usage

## S3 method for class 'fastml_model'
plot(x, ...)

Arguments

x	An object of class fastml_model.
...	Additional arguments (not used).

Value

Displays comparison plots of model performances.

---

Predict Function for fastml_model

Description

Makes predictions on new data using the trained model.

Usage

## S3 method for class 'fastml_model'
predict(object, newdata, ...)

Arguments

object	An object of class fastml_model.
newdata	A data frame containing new data for prediction.
...	Additional arguments (not used).

Value

A vector of predictions.

---

Save Model Function

Description

Saves the trained model object to a file.

Usage

save_model(model, filepath)

Arguments

model	An object of class fastml_model.
filepath	A string specifying the file path to save the model.

Value

No return value, called for its side effect of saving the model object to a file.

---

Summary Function for fastml_model

Description

Provides a detailed summary of the models' performances.

Usage

## S3 method for class 'fastml_model'
summary(object, sort_metric = NULL, ...)

Arguments

object	An object of class fastml_model.
sort_metric	A string specifying which metric to sort the models by. Default is NULL, which prioritizes the optimized metric.
...	Additional arguments (not used).

Value

Prints a summary of the models' performances and displays comparison plots.

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Train Specified Machine Learning Algorithms on the Training Data

Description

Trains specified machine learning algorithms on the preprocessed training data.

Usage

train_models(
  train_data,
  label,
  algorithms,
  resampling_method,
  folds,
  repeats = NULL,
  tune_params,
  metric,
  summaryFunction = NULL,
  seed = 123
)

Arguments

train_data	Preprocessed training data frame.
label	Name of the target variable.
algorithms	Vector of algorithm names to train.
resampling_method	Resampling method for cross-validation (e.g., "cv", "repeatedcv").
folds	Number of folds for cross-validation.
repeats	Number of times to repeat cross-validation (only applicable for methods like "repeatedcv").
tune_params	List of hyperparameter tuning ranges.
metric	The performance metric to optimize.
summaryFunction	A custom summary function for model evaluation. Default is NULL.
seed	An integer value specifying the random seed for reproducibility.

Value

A list of trained model objects.

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Validate and Complete the tuneGrid

Description

Ensures that the tuneGrid includes all required hyperparameters and adjusts it based on cross-validation.

Usage

validate_tuneGrid(tuneGrid, default_params, required_params, resampling_method)

Arguments

tuneGrid	User-provided tuning grid.
default_params	Default hyperparameter ranges.
required_params	Required hyperparameters for the algorithm.
resampling_method	Logical indicating whether cross-validation is enabled.

Value

A validated and possibly modified tuneGrid.

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