multinomial_parameters

user_params

<p>A list of settings and values for vtreat multinomial classification fitting. 
Please see
<a href="https://github.com/WinVector/vtreat/blob/master/Examples/fit_transform/fit_transform_api.md">https://github.com/WinVector/vtreat/blob/master/Examples/fit_transform/fit_transform_api.md</a>,
<code><a rd-options="" href="/link/mkCrossFrameMExperiment?package=vtreat&version=1.6.2" data-mini-rdoc="vtreat::mkCrossFrameMExperiment">mkCrossFrameMExperiment</a></code> and
<code><a rd-options="" href="/link/prepare.multinomial_plan?package=vtreat&version=1.6.2" data-mini-rdoc="vtreat::prepare.multinomial_plan">prepare.multinomial_plan</a></code> for details.</p>

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

multinomial_parameters function

<p>A list of settings and values for vtreat multinomial classification fitting. 
Please see
<a href='https://github.com/WinVector/vtreat/blob/master/Examples/fit_transform/fit_transform_api.md'>https://github.com/WinVector/vtreat/blob/master/Examples/fit_transform/fit_transform_api.md</a>,
<code><a rd-options='' href='mkCrossFrameMExperiment'>mkCrossFrameMExperiment</a></code> and
<code><a rd-options='' href='prepare.multinomial_plan'>prepare.multinomial_plan</a></code> for details.</p>

multinomial_parameters: vtreat multinomial parameters.

Description

Usage

Arguments

Value