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mlexperiments (version 0.0.5)

Machine Learning Experiments

Description

Provides 'R6' objects to perform parallelized hyperparameter optimization and cross-validation. Hyperparameter optimization can be performed with Bayesian optimization (via '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.

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Install

install.packages('mlexperiments')

Monthly Downloads

299

Version

0.0.5

License

GPL (>= 3)

Issues

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Maintainer

Lorenz A. Kapsner

Last Published

March 3rd, 2025

Functions in mlexperiments (0.0.5)

LearnerLm

LearnerLm R6 class
LearnerRpart

LearnerRpart R6 class
MLLearnerBase

R6 Class to construct learners
MLBase

Basic R6 Class for the mlexperiments package
MLTuneParameters

R6 Class to perform hyperparameter tuning experiments
MLCrossValidation

R6 Class to perform cross-validation experiments
LearnerGlm

LearnerGlm R6 class
LearnerKnn

LearnerKnn R6 class
MLNestedCV

R6 Class to perform nested cross-validation experiments
MLExperimentsBase

R6 Class on which the experiment classes are built on
predictions

predictions
metric_types_helper

metric_types_helper
performance

performance
validate_fold_equality

validate_fold_equality
handle_cat_vars

handle_cat_vars
metric

metric