nomogram_shaps2

An example of a data frame for <code>feature_exp</code> argument in
<code>create_nomogram</code> function, must only include feature
explainability value per sample (i.e., SHAP value), where one column is
available for each feature.

dataset

Construct an explainable nomogram for a machine learning (ML) model to improve availability of an ML prediction model in addition to a computer application, particularly in a situation where a computer, a mobile phone, an internet connection, or the application accessibility are unreliable. This package enables a nomogram creation for any ML prediction models, which is conventionally limited to only a linear/logistic regression model. This nomogram may indicate the explainability value per feature, e.g., the Shapley additive explanation value, for each individual. However, this package only allows a nomogram creation for a model using categorical without or with single numerical predictors. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rmlnomogram/blob/master/doc/ml_nomogram_exemplar.html>.

Herdiantri Sufriyana

rmlnomogram

Construct Explainable Nomogram for a Machine Learning Model

Emily Chia-Yu Su

nomogram_shaps2 function

A data frame with 80 rows and 4 columns:<dl>
<dt>cyl.6</dt>
<dd>A predictor with SHAP values.</dd><dt>cyl.8</dt>
<dd>A predictor with SHAP values.</dd><dt>qsec</dt>
<dd>A predictor with SHAP values.</dd><dt>vs.1</dt>
<dd>A predictor with SHAP values.</dd>
</dl>

Format

Nomogram SHAP values using categorical and 1 numerical predictors and binary
outcome — nomogram_shaps2

A data frame with 80 rows and 4 columns:<dl>
<dt>cyl.6</dt>
<dd>A predictor with SHAP values.</dd>

<dt>cyl.8</dt>
<dd>A predictor with SHAP values.</dd>

<dt>qsec</dt>
<dd>A predictor with SHAP values.</dd>

<dt>vs.1</dt>
<dd>A predictor with SHAP values.</dd>


</dl>

nomogram_shaps2: Nomogram SHAP values using categorical and 1 numerical predictors and binary outcome

Description

Usage

Arguments

Format