TuningGrid

Defines control parameters for a tuning grid.

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

TuningGrid function

<dl><dt>size</dt>
<dd>single integer or vector of integers whose positions or names
match the parameters in a model's tuning grid and which specify the number
of values used to construct the grid.</dd>
<dt>random</dt>
<dd>number of unique points to sample at random from the grid
defined by <code>size</code>. If <code>size</code> is a single unnamed integer, then
<code>random = Inf</code> will include all values of all grid parameters in the
constructed grid, whereas <code>random = FALSE</code> will include all values of
default grid parameters.</dd></dl>

Arguments

Tuning Grid Control — TuningGrid

<dl>

<dt>size</dt>
<dd>single integer or vector of integers whose positions or names
match the parameters in a model's tuning grid and which specify the number
of values used to construct the grid.</dd>


<dt>random</dt>
<dd>number of unique points to sample at random from the grid
defined by <code>size</code>. If <code>size</code> is a single unnamed integer, then
<code>random = Inf</code> will include all values of all grid parameters in the
constructed grid, whereas <code>random = FALSE</code> will include all values of
default grid parameters.</dd>

</dl>

TuningGrid: Tuning Grid Control

Description

Usage

Value

Arguments

Details

See Also

Examples