A Unified Toolkit for Feature Selection

Documentation for package ‘featR’ version 0.1.0

fs_bayes	Bayesian feature selection for model optimization
fs_boruta	Feature selection using Boruta
fs_chi	Chi-square feature selection for categorical features
fs_correlation	Correlation-based feature selection
fs_elastic	Elastic Net Feature Selection and Model Training
fs_infogain	Feature Selection via 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 and held-out evaluation
fs_recursivefeature	Recursive feature elimination with held-out evaluation
fs_stepwise	Stepwise linear-regression feature selection via AIC
fs_supervised	Supervised Filter-Based Feature Selection
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-Based Feature Selection
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