predict_forest_weights

Computes forest out-of-sample honest weights for an <code>ocf.forest</code> object.

internal

Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. 'ocf' provides forest-based estimation of the
conditional choice probabilities and the covariates’ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal
and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025)
<doi:10.1080/07474938.2024.2429596>.

Riccardo Di Francesco

Ordered Correlation Forest

predict_forest_weights function

<dl><dt>forest</dt>
<dd>An <code>ocf.forest</code> object.</dd>
<dt>honest_sample</dt>
<dd>Honest sample.</dd>
<dt>test_sample</dt>
<dd>Test sample.</dd></dl>

Arguments

Forest Out-of-Sample Weights — predict_forest_weights

<dl>

<dt>forest</dt>
<dd>An <code>ocf.forest</code> object.</dd>


<dt>honest_sample</dt>
<dd>Honest sample.</dd>


<dt>test_sample</dt>
<dd>Test sample.</dd>

</dl>

predict_forest_weights: Forest Out-of-Sample Weights

Description

Usage

Value

Arguments

Details