oob_predictions

ranger stores the out-of-bag predictions of the training rows on the fitted
object, in training-row order and at no extra cost. They estimate the true
predictive spread to within ~1% where the in-sample predictions understate
it about twofold. Returns NULL whenever they are unavailable, misaligned, or
too sparse to estimate a scale from, so callers fall back to their own path.

internal

Provides methods for imputation and visualization of
missing values. It includes graphical tools to explore the amount, structure
and patterns of missing and/or imputed values, supporting exploratory
data analysis and helping to investigate potential missingness mechanisms
(details in Alfons, Templ and Filzmoser, <doi:10.1007/s11634-011-0102-y>).
The quality of imputations can be assessed visually using a wide range of
univariate, bivariate and multivariate plots.
The package further provides several imputation methods,
including efficient implementations of k-nearest neighbour and hot-deck
imputation (Kowarik and Templ 2013, <doi:10.18637/jss.v074.i07>),
iterative robust model-based multiple
imputation (Templ 2011, <doi:10.1016/j.csda.2011.04.012>;
Templ 2023, <doi:10.3390/math11122729>), and machine learning–based
approaches such as robust GAM-based multiple imputation
(Templ 2024, <doi:10.1007/s11222-024-10429-1>) as well as random forest
and gradient boosting (XGBoost) imputation
(Niederhametner et al., <doi:10.1177/18747655251339401>).
General background and practical guidance on imputation are provided in the
Springer book by
Templ (2023) <doi:10.1007/978-3-031-30073-8>.

Matthias Templ

Visualization and Imputation of Missing Values

Alexander Kowarik

Andreas Alfons

Johannes Gussenbauer

Nina Niederhametner

Eileen Vattheuer

Gregor de Cillia

Bernd Prantner

Wolfgang Rannetbauer

oob_predictions function

<dl><dt>learner</dt>
<dd>A trained mlr3 learner or GraphLearner</dd>
<dt>n</dt>
<dd>Number of training rows the predictions must align with</dd></dl>

Arguments

Out-of-bag predictions of a fitted learner, when it exposes them — oob_predictions

<dl>

<dt>learner</dt>
<dd>A trained mlr3 learner or GraphLearner</dd>


<dt>n</dt>
<dd>Number of training rows the predictions must align with</dd>

</dl>

Out-of-bag predictions of a fitted learner, when it exposes them

oob_predictions: Out-of-bag predictions of a fitted learner, when it exposes them

Description

Usage

Value

Arguments