print.ObliqueForest

Printing an ORSF model tells you:<ul>
<li>Linear combinations: How were these identified?</li>
<li>N observations: Number of rows in training data</li>
<li>N events: Number of events in training data</li>
<li>N trees: Number of trees in the forest</li>
<li>N predictors total: Total number of columns in the predictor matrix</li>
<li>N predictors per node: Number of variables used in linear combinations</li>
<li>Average leaves per tree: A proxy for the depth of your trees</li>
<li>Min observations in leaf: See <code>leaf_min_obs</code> in orsf</li>
<li>Min events in leaf: See <code>leaf_min_events</code> in orsf</li>
<li>OOB stat value: Out-of-bag error after fitting all trees</li>
<li>OOB stat type: How was out-of-bag error computed?</li>
<li>Variable importance: How was variable importance computed?</li>
</ul>

Fit, interpret, and compute predictions with oblique random
forests. Includes support for partial dependence, variable importance,
passing customized functions for variable importance and identification
of linear combinations of features. Methods for the oblique random
survival forest are described in Jaeger et al., (2023)
<DOI:10.1080/10618600.2023.2231048>.

Byron Jaeger

aorsf

Accelerated Oblique Random Forests

Nicholas Pajewski

Sawyer Welden

Christopher Jackson

Marvin Wright

Lukas Burk

print.ObliqueForest function

<dl><dt>x</dt>
<dd>(ObliqueForest) an oblique random survival forest (ORSF; see orsf).</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods (not currently used).</dd></dl>

Arguments

Inspect Forest Parameters — print.ObliqueForest

<dl>

<dt>x</dt>
<dd>(ObliqueForest) an oblique random survival forest (ORSF; see orsf).</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods (not currently used).</dd>

</dl>

print.ObliqueForest: Inspect Forest Parameters

Description

Usage

Value

Arguments

Examples