Result from <code><a href="/link/predict.WrappedModel?package=mlr&version=2.3" rd-options="" data-mini-rdoc="mlr::predict.WrappedModel">predict.WrappedModel</a></code>.
Use <code>as.data.frame</code> to access all information in a convenient format.
The function <code><a href="/link/getProbabilities?package=mlr&version=2.3" rd-options="" data-mini-rdoc="mlr::getProbabilities">getProbabilities</a></code> is useful to access predicted probabilities.The <code>data</code> member of the object contains always the following columns:
<code>id</code>, index numbers of predicted cases from the task, <code>response</code>
either a numeric or a factor, the predicted response values, <code>truth</code>,
either a numeric or a factor, the true target values.
If probabilities were predicted, as many numeric columns as there were classes named
<code>prob.classname</code>. If standard errors were predicted, a numeric column named <code>se</code>.
Object members:
[object Object],[object Object],[object Object],[object Object],[object Object]

Prediction

Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There is
    also an experimental extension for survival analysis, clustering and
    general, example-specific cost-sensitive learning. Generic resampling,
    including cross-validation, bootstrapping and subsampling. Hyperparameter
    tuning with modern optimization techniques, for single- and multi-objective
    problems. Filter and wrapper methods for feature selection. Extension of
    basic learners with additional operations common in machine learning, also
    allowing for easy nested resampling. Most operations can be parallelized.

Prediction: Prediction object.

Description

Arguments