tune_reward_kernel_bandwidth

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

tune_reward_kernel_bandwidth function

<dl><dt>lnr</dt>
<dd>The learner to use for tuning the bandwidth</dd>
<dt>...</dt>
<dd>Refer to the Julia documentation for other parameters</dd></dl>

Arguments

Requires IAI version 2.2 or higher.

IAI Compatibility

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

Conduct the reward kernel bandwidth tuning procedure for a range of starting
bandwidths and return the final tuned values. — tune_reward_kernel_bandwidth

<dl>

<dt>lnr</dt>
<dd>The learner to use for tuning the bandwidth</dd>


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

</dl>

Requires IAI version 2.2 or higher.

tune_reward_kernel_bandwidth: Conduct the reward kernel bandwidth tuning procedure for a range of starting bandwidths and return the final tuned values.

Description

Usage

Arguments

IAI Compatibility

Examples