normalize_score

Normalizes the original score compared to a naive baseline score
The calculation that's being performed depends on the type of model

The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score
that can detect linear or non-linear relationships between two variables.
The score ranges from 0 (no predictive power) to 1 (perfect predictive power).
PPS can be useful for data exploration purposes, in the same way correlation analysis is.
For more information on PPS, see <https://github.com/paulvanderlaken/ppsr>.

Paul van der Laken

ppsr

Predictive Power Score

normalize_score function

<dl><dt>baseline_score</dt>
<dd>float, the evaluation metric score for a naive baseline (model)</dd>
<dt>model_score</dt>
<dd>float, the evaluation metric score for a statistical model</dd>
<dt>type</dt>
<dd>character, type of model</dd></dl>

Arguments

Normalizes the original score compared to a naive baseline score
The calculation that's being performed depends on the type of model — normalize_score

<dl>

<dt>baseline_score</dt>
<dd>float, the evaluation metric score for a naive baseline (model)</dd>


<dt>model_score</dt>
<dd>float, the evaluation metric score for a statistical model</dd>


<dt>type</dt>
<dd>character, type of model</dd>

</dl>

normalize_score: Normalizes the original score compared to a naive baseline score The calculation that's being performed depends on the type of model

Description

Usage

Value

Arguments