ClassificationExample

Synthetic data used to illustrate how to use TSLA with classification.

data

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>).

Jianmin Chen

TSLA

Tree-Guided Rare Feature Selection and Logic Aggregation

Kun Chen

ClassificationExample function

List containing the following elements:<dl>
 <dt>tree.org</dt>
<dd>Original tree structure with 42 leaf nodes and 5 different levels.</dd> <dt>x.org</dt>
<dd>Original design matrix with 42 binary features and 400 observations.</dd> <dt>x1</dt>
<dd>Unpenalized covariate.</dd> <dt>y</dt>
<dd>Continuous response of length 400.</dd>
</dl>

Format

Synthesic for the classification example — ClassificationExample

List containing the following elements:<dl>
 <dt>tree.org</dt>
<dd>Original tree structure with 42 leaf nodes and 5 different levels.</dd>

 <dt>x.org</dt>
<dd>Original design matrix with 42 binary features and 400 observations.</dd>

 <dt>x1</dt>
<dd>Unpenalized covariate.</dd>

 <dt>y</dt>
<dd>Continuous response of length 400.</dd>


</dl>

ClassificationExample: Synthesic for the classification example

Description

Usage

Arguments

Format