Discretize

Functions for MultiDimensional Feature Selection (MDFS):
calculating multidimensional information gains, scoring variables,
finding important variables, plotting selection results.
This package includes an optional CUDA implementation that speeds up
information gain calculation using NVIDIA GPGPUs.
R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.

Radosław Piliszek

MDFS

MultiDimensional Feature Selection

Krzysztof Mnich

Paweł Tabaszewski

Szymon Migacz

Andrzej Sułecki

Discretize function

<dl><dt>data</dt>
<dd>input data where columns are variables and rows are observations (all numeric)</dd>
<dt>variable.idx</dt>
<dd>variable index (as it appears in <code>data</code>)</dd>
<dt>divisions</dt>
<dd>number of divisions</dd>
<dt>discretization.nr</dt>
<dd>discretization number (positive integer)</dd>
<dt>seed</dt>
<dd>seed for PRNG</dd>
<dt>range</dt>
<dd>discretization range</dd></dl>

Arguments

Discretize variable on demand — Discretize

<dl>

<dt>data</dt>
<dd>input data where columns are variables and rows are observations (all numeric)</dd>


<dt>variable.idx</dt>
<dd>variable index (as it appears in <code>data</code>)</dd>


<dt>divisions</dt>
<dd>number of divisions</dd>


<dt>discretization.nr</dt>
<dd>discretization number (positive integer)</dd>


<dt>seed</dt>
<dd>seed for PRNG</dd>


<dt>range</dt>
<dd>discretization range</dd>

</dl>

Discretize variable on demand

Discretize: Discretize variable on demand

Description

Usage

Value

Arguments

Examples