set_monitor

<a rd-options="=controls" href="/link/control?package=MachineShop&version=3.2.0&to=%3Dcontrols" data-mini-rdoc="=controls::control">control</a> object.

control

logical indicating whether to display a progress bar during
resampling if a computing cluster is not registered or is registered with
the doSNOW package.

progress

logical indicating whether to enable verbose messages which
may be useful for trouble shooting.

verbose

Set parameters that control the monitoring of 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.

set_monitor: Resampling Monitoring Control

Description

Usage

Arguments

Value

See Also

Examples