set_predict

Set parameters that control prediction during resample estimation of model
performance.

Meta-package for statistical and machine learning with a unified
interface for model fitting, prediction, performance assessment, and
presentation of results.  Approaches for model fitting and prediction of
numerical, categorical, or censored time-to-event outcomes include
traditional regression models, regularization methods, tree-based methods,
support vector machines, neural networks, ensembles, data preprocessing,
filtering, and model tuning and selection.  Performance metrics are provided
for model assessment and can be estimated with independent test sets, split
sampling, cross-validation, or bootstrap resampling.  Resample estimation
can be executed in parallel for faster processing and nested in cases of
model tuning and selection.  Modeling results can be summarized with
descriptive statistics; calibration curves; variable importance; partial
dependence plots; confusion matrices; and ROC, lift, and other performance
curves.

Brian Smith

MachineShop

Machine Learning Models and Tools

set_predict function

<dl><dt>object</dt>
<dd>control object.</dd>
<dt>times, distr, method</dt>
<dd>arguments passed to <code><a href="/link/predict?package=MachineShop&version=3.8.0" data-mini-rdoc="MachineShop::predict">predict</a></code>.</dd>
<dt>...</dt>
<dd>arguments passed to other methods.</dd></dl>

Arguments

Resampling Prediction Control — set_predict

<dl>

<dt>object</dt>
<dd>control object.</dd>


<dt>times, distr, method</dt>
<dd>arguments passed to <code><a href='https://rdrr.io/r/stats/predict.html'>predict</a></code>.</dd>


<dt>...</dt>
<dd>arguments passed to other methods.</dd>

</dl>

set_predict: Resampling Prediction Control

Description

Usage

Value

Arguments

See Also

Examples