kWayCrossValidation

k-fold cross validation, a splitFunction in the sense of vtreat::buildEvalSets

A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner.
'vtreat' prepares variables so that data has fewer exceptional cases, making
it easier to safely use models in production. Common problems 'vtreat' defends
against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new
categorical levels (levels seen during application, but not during training). Reference:
"'vtreat': a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, <DOI:10.5281/zenodo.1173313>.

John Mount

vtreat

A Statistically Sound 'data.frame' Processor/Conditioner

Nina Zumel

 Win-Vector LLC

kWayCrossValidation function

<dl><dt>nRows</dt>
<dd>number of rows to split (&gt;1).</dd>
<dt>nSplits</dt>
<dd>number of groups to split into (&gt;1,&lt;=nRows).</dd>
<dt>dframe</dt>
<dd>original data frame (ignored).</dd>
<dt>y</dt>
<dd>numeric outcome variable (ignored).</dd></dl>

Arguments

k-fold cross validation, a splitFunction in the sense of vtreat::buildEvalSets — kWayCrossValidation

<dl>

<dt>nRows</dt>
<dd>number of rows to split (&gt;1).</dd>


<dt>nSplits</dt>
<dd>number of groups to split into (&gt;1,&lt;=nRows).</dd>


<dt>dframe</dt>
<dd>original data frame (ignored).</dd>


<dt>y</dt>
<dd>numeric outcome variable (ignored).</dd>

</dl>

kWayCrossValidation: k-fold cross validation, a splitFunction in the sense of vtreat::buildEvalSets

Description

Usage

Value

Arguments

Examples