Package: mlr3learners 0.7.0
Marc Becker
mlr3learners:Recommended Learners for 'mlr3'
Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.
Authors:
mlr3learners_0.7.0.tar.gz
mlr3learners_0.7.0.tar.gz(r-4.5-noble)mlr3learners_0.7.0.tar.gz(r-4.4-noble)
mlr3learners_0.7.0.tgz(r-4.4-emscripten)mlr3learners_0.7.0.tgz(r-4.3-emscripten)
mlr3learners.pdf |mlr3learners.html✨
mlr3learners/json (API)
NEWS
| # Installmlr3learners in R: |
| install.packages('mlr3learners',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mlr-org/mlr3learners/issues
Last updated 7 days agofrom:373fdf1d2a
Exports:LearnerClassifCVGlmnetLearnerClassifGlmnetLearnerClassifKKNNLearnerClassifLDALearnerClassifLogRegLearnerClassifMultinomLearnerClassifNaiveBayesLearnerClassifNnetLearnerClassifQDALearnerClassifRangerLearnerClassifSVMLearnerClassifXgboostLearnerRegrCVGlmnetLearnerRegrGlmnetLearnerRegrKKNNLearnerRegrKMLearnerRegrLMLearnerRegrNnetLearnerRegrRangerLearnerRegrSVMLearnerRegrXgboost
Dependencies:backportscheckmatecodetoolsdata.tabledigestevaluatefuturefuture.applyglobalslgrlistenvmlbenchmlr3mlr3measuresmlr3miscpalmerpenguinsparadoxparallellyPRROCR6RhpcBLASctluuid
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| mlr3learners: Recommended Learners for 'mlr3' | mlr3learners-package mlr3learners |
| GLM with Elastic Net Regularization Classification Learner | LearnerClassifCVGlmnet mlr_learners_classif.cv_glmnet |
| GLM with Elastic Net Regularization Classification Learner | LearnerClassifGlmnet mlr_learners_classif.glmnet |
| k-Nearest-Neighbor Classification Learner | LearnerClassifKKNN mlr_learners_classif.kknn |
| Linear Discriminant Analysis Classification Learner | LearnerClassifLDA mlr_learners_classif.lda |
| Logistic Regression Classification Learner | LearnerClassifLogReg mlr_learners_classif.log_reg |
| Multinomial log-linear learner via neural networks | LearnerClassifMultinom mlr_learners_classif.multinom |
| Naive Bayes Classification Learner | LearnerClassifNaiveBayes mlr_learners_classif.naive_bayes |
| Classification Neural Network Learner | LearnerClassifNnet mlr_learners_classif.nnet |
| Quadratic Discriminant Analysis Classification Learner | LearnerClassifQDA mlr_learners_classif.qda |
| Ranger Classification Learner | LearnerClassifRanger mlr_learners_classif.ranger |
| Support Vector Machine | LearnerClassifSVM mlr_learners_classif.svm |
| Extreme Gradient Boosting Classification Learner | LearnerClassifXgboost mlr_learners_classif.xgboost |
| GLM with Elastic Net Regularization Regression Learner | LearnerRegrCVGlmnet mlr_learners_regr.cv_glmnet |
| GLM with Elastic Net Regularization Regression Learner | LearnerRegrGlmnet mlr_learners_regr.glmnet |
| k-Nearest-Neighbor Regression Learner | LearnerRegrKKNN mlr_learners_regr.kknn |
| Kriging Regression Learner | LearnerRegrKM mlr_learners_regr.km |
| Linear Model Regression Learner | LearnerRegrLM mlr_learners_regr.lm |
| Neural Network Regression Learner | LearnerRegrNnet mlr_learners_regr.nnet |
| Ranger Regression Learner | LearnerRegrRanger mlr_learners_regr.ranger |
| Support Vector Machine | LearnerRegrSVM mlr_learners_regr.svm |
| Extreme Gradient Boosting Regression Learner | LearnerRegrXgboost mlr_learners_regr.xgboost |
