Tree-Guided Rare Feature Selection and Logic Aggregation
Calculate group norms
Get coefficients from a fitted TSLA model
Cross validation for TSLA
Tree-guided reparameterization
Generate aggregated features
Tree-guided expansion
Get performance metrics for classification
Plot aggregated structure
Prediction from cross validation
Prediction from TSLA with new data
Tree-Guided Rare Feature Selection and Logic Aggregation
Solve the TSLA optimization problem
Implementation of the tree-guided feature selection and logic aggregation approach introduced in Chen et al. (2024) <doi:10.1080/01621459.2024.2326621>. The method enables the selection and aggregation of large-scale rare binary features with a known hierarchical structure using a convex, linearly-constrained regularized regression framework. The package facilitates the application of this method to both linear regression and binary classification problems by solving the optimization problem via the smoothing proximal gradient descent algorithm (Chen et al. (2012) <doi:10.1214/11-AOAS514>).