nomogram_features

An example of a data frame for <code>sample_features</code> argument in
<code>create_nomogram</code> function, must only include all possible
combinations of feature values, 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_features function

A data frame with 16 rows and 4 columns:<dl>
<dt>cyl.6</dt>
<dd>A categorical predictor with values of 0 and 1.</dd><dt>cyl.8</dt>
<dd>A categorical predictor with values of 0 and 1.</dd><dt>qsec.1</dt>
<dd>A categorical predictor with values of 0 and 1.</dd><dt>vs.1</dt>
<dd>A categorical predictor with values of 0 and 1.</dd>
</dl>

Format

Nomogram features using categorical predictors — nomogram_features

A data frame with 16 rows and 4 columns:<dl>
<dt>cyl.6</dt>
<dd>A categorical predictor with values of 0 and 1.</dd>

<dt>cyl.8</dt>
<dd>A categorical predictor with values of 0 and 1.</dd>

<dt>qsec.1</dt>
<dd>A categorical predictor with values of 0 and 1.</dd>

<dt>vs.1</dt>
<dd>A categorical predictor with values of 0 and 1.</dd>


</dl>

nomogram_features: Nomogram features using categorical predictors

Description

Usage

Arguments

Format