predict_TSLA

Generate prediction for the response.

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

predict_TSLA function

<dl><dt>object</dt>
<dd>A fit output from <code>TSLA.fit()</code>.</dd>
<dt>X_1_new</dt>
<dd>New unpenalized features in matrix form.</dd>
<dt>X_2_new</dt>
<dd>New binary features in matrix form.</dd>
<dt>type</dt>
<dd>Two options: "response" or "link". The two options only
differ for <code>family="logit"</code>.</dd>
<dt>...</dt>
<dd>Other parameters.</dd></dl>

Arguments

Prediction from TSLA with new data — predict_TSLA

<dl>

<dt>object</dt>
<dd>A fit output from <code>TSLA.fit()</code>.</dd>


<dt>X_1_new</dt>
<dd>New unpenalized features in matrix form.</dd>


<dt>X_2_new</dt>
<dd>New binary features in matrix form.</dd>


<dt>type</dt>
<dd>Two options: "response" or "link". The two options only
differ for <code>family="logit"</code>.</dd>


<dt>...</dt>
<dd>Other parameters.</dd>

</dl>

predict_TSLA: Prediction from TSLA with new data

Description

Usage

Value

Arguments