executeBinning

Provides functions to fit Accurate Generalized Linear Model (AGLM) models, visualize them, and predict for new data. AGLM is defined as a regularized GLM which applies a sort of feature transformations using a discretization of numerical features and specific coding methodologies of dummy variables. For more information on AGLM, see Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa (2020) <https://www.institutdesactuaires.com/global/gene/link.php?doc_id=16273&fg=1>.

Kenji Kondo

aglm

Accurate Generalized Linear Model

Kazuhisa Takahashi

Hikari Banno

executeBinning function

<dl><dt>x_vec</dt>
<dd>The data to be binned.</dd>
<dt>breaks</dt>
<dd>A numeric vector representing breaks of bins (If <code>NULL</code>, automatically generated).</dd>
<dt>nbin.max</dt>
<dd>The maximum number of bins (used only if <code>breaks=NULL</code>).</dd>
<dt>method</dt>
<dd><code>"freq"</code> for equal frequency binning or <code>"width"</code> for equal width binning (used only if <code>breaks=NULL</code>).</dd></dl>

Arguments

Author

Binning the data to given bins. — executeBinning

<dl>

<dt>x_vec</dt>
<dd>The data to be binned.</dd>


<dt>breaks</dt>
<dd>A numeric vector representing breaks of bins (If <code>NULL</code>, automatically generated).</dd>


<dt>nbin.max</dt>
<dd>The maximum number of bins (used only if <code>breaks=NULL</code>).</dd>


<dt>method</dt>
<dd><code>"freq"</code> for equal frequency binning or <code>"width"</code> for equal width binning (used only if <code>breaks=NULL</code>).</dd>

</dl>

executeBinning: Binning the data to given bins.

Description

Usage

Value

Arguments

Author