Package: mlexperiments 0.0.4
mlexperiments: Machine Learning Experiments
Provides 'R6' objects to perform parallelized hyperparameter optimization and cross-validation. Hyperparameter optimization can be performed with Bayesian optimization (via 'ParBayesianOptimization' <https://cran.r-project.org/package=ParBayesianOptimization>) 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:
mlexperiments_0.0.4.tar.gz
mlexperiments_0.0.4.zip(r-4.5)mlexperiments_0.0.4.zip(r-4.4)mlexperiments_0.0.4.zip(r-4.3)
mlexperiments_0.0.4.tgz(r-4.4-any)mlexperiments_0.0.4.tgz(r-4.3-any)
mlexperiments_0.0.4.tar.gz(r-4.5-noble)mlexperiments_0.0.4.tar.gz(r-4.4-noble)
mlexperiments_0.0.4.tgz(r-4.4-emscripten)mlexperiments_0.0.4.tgz(r-4.3-emscripten)
mlexperiments.pdf |mlexperiments.html✨
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
cross-validationexperimenthyperparameter-optimizationhyperparameter-tuningmachine-learningnested
Last updated 5 months agofrom:58872947c2. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 02 2024 |
R-4.5-win | OK | Nov 02 2024 |
R-4.5-linux | OK | Nov 02 2024 |
R-4.4-win | OK | Nov 02 2024 |
R-4.4-mac | OK | Nov 02 2024 |
R-4.3-win | OK | Nov 02 2024 |
R-4.3-mac | OK | Nov 02 2024 |
Exports:handle_cat_varsLearnerGlmLearnerKnnLearnerLmLearnerRpartmetricmetric_types_helperMLCrossValidationMLLearnerBaseMLNestedCVMLTuneParametersperformancepredictionsvalidate_fold_equality
Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecrayondata.tabledigestdoParallelevaluatefansifarverfastmapfontawesomeforeachforeignFormulafsggplot2gluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonlitekdryknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigprettyunitsprogressR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapisassscalessplitToolsstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml
KNN: Binary Classification
Rendered frommlexperiments_knn_binary.qmd
usingquarto::html
on Nov 02 2024.Last update: 2024-05-29
Started: 2024-05-29
KNN: Multiclass Classification
Rendered frommlexperiments_knn_multiclass.qmd
usingquarto::html
on Nov 02 2024.Last update: 2024-05-29
Started: 2024-05-29
mlexperiments: Getting Started
Rendered frommlexperiments_starter.qmd
usingquarto::html
on Nov 02 2024.Last update: 2024-05-29
Started: 2024-05-29
rpart: Binary Classification
Rendered frommlexperiments_rpart_binary.qmd
usingquarto::html
on Nov 02 2024.Last update: 2024-05-29
Started: 2024-05-29
rpart: Multiclass Classification
Rendered frommlexperiments_rpart_multiclass.qmd
usingquarto::html
on Nov 02 2024.Last update: 2024-05-29
Started: 2024-05-29
rpart: Regression
Rendered frommlexperiments_rpart_regression.qmd
usingquarto::html
on Nov 02 2024.Last update: 2024-05-29
Started: 2024-05-29
Readme and manuals
Help Manual
Help page | Topics |
---|---|
handle_cat_vars | handle_cat_vars |
LearnerGlm R6 class | LearnerGlm |
LearnerKnn R6 class | LearnerKnn |
LearnerLm R6 class | LearnerLm |
LearnerRpart R6 class | LearnerRpart |
metric | metric |
metric_types_helper | metric_types_helper |
Basic R6 Class for the mlexperiments package | MLBase |
R6 Class to perform cross-validation experiments | MLCrossValidation |
R6 Class on which the experiment classes are built on | MLExperimentsBase |
R6 Class to construct learners | MLLearnerBase |
R6 Class to perform nested cross-validation experiments | MLNestedCV |
R6 Class to perform hyperparameter tuning experiments | MLTuneParameters |
performance | performance |
predictions | predictions |
validate_fold_equality | validate_fold_equality |