set_positive_class

Checks and sets the positive class for binary classification in a tabular dataset.

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

set_positive_class function

<dl><dt>x</dt>
<dd>Tabular data, i.e. data.frame, data.table, or tbl_df (tibble): Input dataset. The last column will be treated as the outcome variable.</dd>
<dt>positive_class</dt>
<dd>Character scalar: The name of the positive class. Must be one of the levels of the outcome variable.</dd></dl>

Arguments

Author

Set positive class — set_positive_class

<dl>

<dt>x</dt>
<dd>Tabular data, i.e. data.frame, data.table, or tbl_df (tibble): Input dataset. The last column will be treated as the outcome variable.</dd>


<dt>positive_class</dt>
<dd>Character scalar: The name of the positive class. Must be one of the levels of the outcome variable.</dd>

</dl>

set_positive_class: Set positive class

Description

Usage

Value

Arguments

Author

Examples