set_monitor

set_monitor.MLControl

set_monitor.MLOptimization

set_monitor.ModelSpecification

resampling <a rd-options="=controls" href="/link/control?package=MachineShop&version=3.5.0&to=%3Dcontrols" data-mini-rdoc="=controls::control">control</a>,
tuning parameter <a rd-options="=set_optim" href="/link/optimization?package=MachineShop&version=3.5.0&to=%3Dset_optim" data-mini-rdoc="=set_optim::optimization">optimization</a>, or model
<a rd-options="=ModelSpecification" href="/link/specification?package=MachineShop&version=3.5.0&to=%3DModelSpecification" data-mini-rdoc="=ModelSpecification::specification">specification</a> object.

object

arguments passed from the <code>ModelSpecification</code> method to the
others.

logical indicating whether to display iterative progress
during resampling or optimization. In the case of resampling, a progress
bar will be displayed if a computing cluster is not registered or is
registered with the doSNOW package.

progress

numeric or logical value specifying the level of progress
detail to print, with 0 (<code>FALSE</code>) indicating none and 1 (<code>TRUE</code>)
or higher indicating increasing amounts of detail.

verbose

character string specifying the monitoring parameters to set as
<code>"all"</code>, <code>"control"</code>, or optimization (<code>"optim"</code>).

which

Set parameters that control the monitoring of resample estimation of model
performance and of tuning parameter optimization.

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_monitor function

<dl><dt>object</dt>
<dd>resampling control,
tuning parameter optimization, or model
specification object.</dd>
<dt>...</dt>
<dd>arguments passed from the <code>ModelSpecification</code> method to the
others.</dd>
<dt>progress</dt>
<dd>logical indicating whether to display iterative progress
during resampling or optimization. In the case of resampling, a progress
bar will be displayed if a computing cluster is not registered or is
registered with the doSNOW package.</dd>
<dt>verbose</dt>
<dd>numeric or logical value specifying the level of progress
detail to print, with 0 (<code>FALSE</code>) indicating none and 1 (<code>TRUE</code>)
or higher indicating increasing amounts of detail.</dd>
<dt>which</dt>
<dd>character string specifying the monitoring parameters to set as
<code>"all"</code>, <code>"control"</code>, or optimization (<code>"optim"</code>).</dd></dl>

Arguments

Training Parameters Monitoring Control — set_monitor

<dl>

<dt>object</dt>
<dd>resampling control,
tuning parameter optimization, or model
specification object.</dd>


<dt>...</dt>
<dd>arguments passed from the <code>ModelSpecification</code> method to the
others.</dd>


<dt>progress</dt>
<dd>logical indicating whether to display iterative progress
during resampling or optimization. In the case of resampling, a progress
bar will be displayed if a computing cluster is not registered or is
registered with the doSNOW package.</dd>


<dt>verbose</dt>
<dd>numeric or logical value specifying the level of progress
detail to print, with 0 (<code>FALSE</code>) indicating none and 1 (<code>TRUE</code>)
or higher indicating increasing amounts of detail.</dd>


<dt>which</dt>
<dd>character string specifying the monitoring parameters to set as
<code>"all"</code>, <code>"control"</code>, or optimization (<code>"optim"</code>).</dd>

</dl>

set_monitor: Training Parameters Monitoring Control

Description

Usage

Value

Arguments

See Also

Examples