A Unified Toolkit for Feature Selection

Documentation for package ‘featR’ version 0.2.0

fs_bayes	Bayesian predictor-subset selection with brms and LOO
fs_boruta	All-relevant feature selection with Boruta
fs_chi	Chi-squared tests for categorical features
fs_correlation	Find and prune highly correlated variables
fs_elastic	Elastic-net feature selection with tuned alpha and lambda
fs_infogain	Rank features by information gain
fs_lasso	Lasso feature selection with cross-validation
fs_mars	MARS (earth) feature selection
fs_pca	Principal component analysis with tidy results and optional plotting
fs_randomforest	Random forest importance with a held-out evaluation
fs_recursivefeature	Recursive feature elimination with held-out evaluation
fs_stepwise	Stepwise linear-regression feature selection by AIC
fs_supervised	Supervised filter that scores each feature against a target
fs_svd	Singular value decomposition with optional scaling and truncation
fs_svm	Train and evaluate an SVM, with optional SVM-RFE feature selection
fs_unsupervised	Unsupervised filter that scores each feature without a target
print.fs_result	Print a featR result
selected	Extract the selected features from a featR result
selected.fs_result	Extract the selected features from a featR result
summary.fs_result	Summarize a featR result