varimp_table

Combine the variable importance measures of a model into one table: one
row per predictor or model term and one column per measure.

Machine learning and visualization package with an 'S7' backend featuring
comprehensive type checking and validation, observable runs, and auditable outputs.
The functional API provides one-call access to supervised and unsupervised learning
through train(), cluster(), and decomp(). All configuration steps are performed using
setup functions and validated. A single call to train() can handle preprocessing,
decomposition, hyperparameter tuning, model stacking, and testing with nested resampling.
Supports 'data.frame', 'data.table', and 'tibble' inputs and parallel execution. Plot
methods for trained and resampled supervised models draw interactive figures through
'rtemis.draw'. Includes native implementations of SuperLearner, RuleFit, and Linear
Additive Trees, and supports conformal prediction. The package first appeared in
E.D. Gennatas (2017)
<https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.

Efstathios D. Gennatas

rtemis

Schema-Driven Machine Learning and Visualization

E.D. Gennatas

varimp_table function

<dl><dt>x</dt>
<dd><code>VariableImportance</code> object, as returned by get_varimp.</dd></dl>

Arguments

Author

Variable importance as one table — varimp_table

<dl>

<dt>x</dt>
<dd><code>VariableImportance</code> object, as returned by get_varimp.</dd>

</dl>

varimp_table: Variable importance as one table

Description

Usage

Value

Arguments

Author

Details

Examples