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.

Machine Learning Models and Tools

package-ubuntu-20.04-r-4.0.5#cran#MachineShop-2.8.0

<p>Function to coerce an <code>MLModelFit</code> object to an <code>MLModel</code>.
</p>

as.MLModel(x, ...)

## S3 method for class 'MLModelFit'
as.MLModel(x, ...)

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
<p>model <a href="fit-methods.html">fit</a> result.</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>arguments passed to other methods.</p>
</td>
</tr>
</table>

<p><code>MLModel</code> class object.
</p>

package-ubuntu-20.04-r-4.0.5#cran#MachineShop-2.8.0#docs#as.MLModel

`x`	model fit result.
`...`	arguments passed to other methods.

as.MLModel

Description

Usage

Arguments

Value

MachineShop

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