fit_and_expand

Julia Equivalent:
<a href="https://docs.interpretable.ai/v3.2.2/OptImpute/reference/#IAI.fit_and_expand!"><code>IAI.fit_and_expand!</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 

fit_and_expand function

<dl><dt>lnr</dt>
<dd>The learner to use for imputation.</dd>
<dt>X</dt>
<dd>The dataframe in which to impute missing values.</dd>
<dt>...</dt>
<dd>Refer to the Julia documentation for available parameters.</dd></dl>

Arguments

Requires IAI version 3.0 or higher.

IAI Compatibility

Julia Equivalent:
<a href='https://docs.interpretable.ai/v3.2.2/OptImpute/reference/#IAI.fit_and_expand!'><code>IAI.fit_and_expand!</code></a>

Fit an imputation learner with training features and create adaptive
indicator features to encode the missing pattern — fit_and_expand

<dl>

<dt>lnr</dt>
<dd>The learner to use for imputation.</dd>


<dt>X</dt>
<dd>The dataframe in which to impute missing values.</dd>


<dt>...</dt>
<dd>Refer to the Julia documentation for available parameters.</dd>

</dl>

Requires IAI version 3.0 or higher.

fit_and_expand: Fit an imputation learner with training features and create adaptive indicator features to encode the missing pattern

Description

Usage

Arguments

IAI Compatibility

Examples