dt_nunique_perfeat

Number of unique values per feature

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

dt_nunique_perfeat function

<dl><dt>x</dt>
<dd>data.table: Input data.table.</dd>
<dt>excludeNA</dt>
<dd>Logical: If TRUE, exclude NA values.</dd>
<dt>limit</dt>
<dd>Integer: Print up to this many features. Set to -1L to print all.</dd>
<dt>verbosity</dt>
<dd>Integer: If &gt; 0, print output to console.</dd></dl>

Arguments

Author

Number of unique values per feature — dt_nunique_perfeat

<dl>

<dt>x</dt>
<dd>data.table: Input data.table.</dd>


<dt>excludeNA</dt>
<dd>Logical: If TRUE, exclude NA values.</dd>


<dt>limit</dt>
<dd>Integer: Print up to this many features. Set to -1L to print all.</dd>


<dt>verbosity</dt>
<dd>Integer: If &gt; 0, print output to console.</dd>

</dl>

dt_nunique_perfeat: Number of unique values per feature

Description

Usage

Value

Arguments

Author

Examples