Package: mlexperiments 1.0.0

mlexperiments: Machine Learning Experiments

Provides 'R6' objects to perform parallelized hyperparameter optimization and cross-validation. Hyperparameter optimization can be performed with Bayesian optimization (via 'rBayesianOptimization' <https://cran.r-project.org/package=rBayesianOptimization>) and grid search. The optimized hyperparameters can be validated using k-fold cross-validation. Alternatively, hyperparameter optimization and validation can be performed with nested cross-validation. While 'mlexperiments' focuses on core wrappers for machine learning experiments, additional learner algorithms can be supplemented by inheriting from the provided learner base class.

Authors:Lorenz A. Kapsner [cre, aut, cph]

mlexperiments_1.0.0.tar.gz
mlexperiments_1.0.0.zip(r-4.7)mlexperiments_1.0.0.zip(r-4.6)mlexperiments_1.0.0.zip(r-4.5)
mlexperiments_1.0.0.tgz(r-4.6-any)mlexperiments_1.0.0.tgz(r-4.5-any)
mlexperiments_1.0.0.tar.gz(r-4.7-any)mlexperiments_1.0.0.tar.gz(r-4.6-any)
mlexperiments_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mlexperiments/json (API)

# Install 'mlexperiments' in R:
install.packages('mlexperiments', repos = c('https://kapsner.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kapsner/mlexperiments/issues

On CRAN:

Conda:

cross-validationexperimenthyperparameter-optimizationhyperparameter-tuningmachine-learningnestedquarto

7.35 score 5 stars 2 packages 50 scripts 595 downloads 14 exports 66 dependencies

Last updated from:ea1cb9dc1a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK402
source / vignettesOK229
linux-release-x86_64OK406
macos-release-arm64OK365
macos-oldrel-arm64OK315
windows-develOK406
windows-releaseOK376
windows-oldrelOK391
wasm-releaseOK154

Exports:handle_cat_varsLearnerGlmLearnerKnnLearnerLmLearnerRpartmetricmetric_types_helperMLCrossValidationMLLearnerBaseMLNestedCVMLTuneParametersperformancepredictionsvalidate_fold_equality

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecpp11crayondata.tabledigestdoParallelevaluatefarverfastmapfontawesomeforeachforeignFormulafsggplot2gluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonlitekdryknitrlabelinglifecyclemagrittrmemoisemimennetpkgconfigprettyunitsprogressR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapiS7sassscalessplitToolsstringistringrtinytexvctrsviridisLitewithrxfunyaml

mlexperiments: Getting Started
General Overview | Steps to Prepare an Algorithm for Use with mlexperiments | The fit Method | The predict Method | The cross_validation Method | The bayesian_scoring_function Method | Finally, Create an R6 Class for the Learner | Examples | Preparations | Hyperparameter Tuning | Bayesian Tuning | Grid Search | Cross-Validation | Nested Cross-Validation | Inner Bayesian Optimization | Inner Grid Search
Rendered frommlexperiments_starter.qmdusingquarto::html

Last update: 2026-01-16
Started: 2024-05-29

KNN: Binary Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization
Rendered frommlexperiments_knn_binary.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

KNN: Multiclass Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization
Rendered frommlexperiments_knn_multiclass.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

rpart: Binary Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Comparison with Logistic Regression | Test Fold Equality | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommlexperiments_rpart_binary.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

rpart: Multiclass Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Appendix I: Grid-Search with Target Weigths | Appendix II: k-Fold Cross Validation with Target Weigths
Rendered frommlexperiments_rpart_multiclass.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

rpart: Regression
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Comparison with Linear Regression | Test Fold Equality | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommlexperiments_rpart_regression.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29