predict_proba.classification_learner

Julia Equivalent:
<a href="https://docs.interpretable.ai/v3.2.2/IAIBase/reference/#IAI.predict_proba-Tuple%7BClassificationLearner%2C%20Union%7BDataFrames.AbstractDataFrame%2C%20AbstractMatrix%7B%3C%3AReal%7D%7D%7D"><code>IAI.predict_proba</code></a>

An interface to the algorithms of 'Interpretable AI'
<https://www.interpretable.ai> from the R programming language.
'Interpretable AI' provides various modules, including 'Optimal Trees' for
classification, regression, prescription and survival analysis, 'Optimal
Imputation' for missing data imputation and outlier detection, and 'Optimal
Feature Selection' for exact sparse regression. The 'iai' package is an
open-source project. The 'Interpretable AI' software modules are proprietary
products, but free academic and evaluation licenses are available.

Jack Dunn

Interface to 'Interpretable AI' Modules

Ying Zhuo

Interpretable AI LLC 

predict_proba.classification_learner function

<dl><dt>obj</dt>
<dd>The learner or grid to use for prediction.</dd>
<dt>X</dt>
<dd>The features of the data.</dd>
<dt>...</dt>
<dd>Additional arguments (unused)</dd></dl>

Arguments

Julia Equivalent:
<a href='https://docs.interpretable.ai/v3.2.2/IAIBase/reference/#IAI.predict_proba-Tuple%7BClassificationLearner%2C%20Union%7BDataFrames.AbstractDataFrame%2C%20AbstractMatrix%7B%3C%3AReal%7D%7D%7D'><code>IAI.predict_proba</code></a>

Return the probabilities of class membership predicted by a classification
learner for each point in the features — predict_proba.classification_learner

<dl>

<dt>obj</dt>
<dd>The learner or grid to use for prediction.</dd>


<dt>X</dt>
<dd>The features of the data.</dd>


<dt>...</dt>
<dd>Additional arguments (unused)</dd>

</dl>

predict_proba.classification_learner: Return the probabilities of class membership predicted by a classification learner for each point in the features

Description

Usage

Arguments

Examples