pac.partition

A method of partitioning data between training and testing sets based on the fraction of data used for training

data

machine-learning

Provides a broad-view perspective on data via
linear mapping of data onto a radial coordinate system. The package
contains functions to visualize the residual values of linear
regression and Cartesian data in the defined radial scheme. See the
'pacviz' documentation page for more information:
<https://pacviz.sriley.dev/>.

Spencer Riley

pacviz

Pac-Man Visualization Package

pac.partition function

<dl><dt>x, y</dt>
<dd>Numeric data</dd>
<dt>l</dt>
<dd>Numeric labels data</dd>
<dt>train_size</dt>
<dd>Fraction of total data that the SVM will train on</dd>
<dt>rand_state</dt>
<dd>Value of the random state used to set the seed</dd></dl>

Arguments

Machine learning data partition — pac.partition

<dl>

<dt>x, y</dt>
<dd>Numeric data</dd>


<dt>l</dt>
<dd>Numeric labels data</dd>


<dt>train_size</dt>
<dd>Fraction of total data that the SVM will train on</dd>


<dt>rand_state</dt>
<dd>Value of the random state used to set the seed</dd>

</dl>

pac.partition: Machine learning data partition

Description

Usage

Value

Arguments