prune

Provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe.

KS Srikanth

tidyrules

Utilities to Retrieve Rulelists from Model Fits, Filter, Prune,
Reorder and Predict on Unseen Data

Srikanth Komala Sheshachala

Amith Kumar Ullur Raghavendra

prune function

<dl><dt>tree</dt>
<dd>A rulelist</dd>
<dt>...</dt>
<dd>See prune.rulelist</dd></dl>

Arguments

prune is re-export of generics::prune from
tidyrules package — prune

<dl>

<dt>tree</dt>
<dd>A rulelist</dd>


<dt>...</dt>
<dd>See prune.rulelist</dd>

</dl>

prune: `prune` is re-export of generics::prune from tidyrules package

Description

Usage

Arguments

See Also