setPredictType

[<code><a rd-options="" href="/link/Learner?package=mlr&version=2.9" data-mini-rdoc="mlr::Learner">Learner</a></code> | <code>character(1)</code>]
The learner.
If you pass a string the learner will be created via <code><a rd-options="" href="/link/makeLearner?package=mlr&version=2.9" data-mini-rdoc="mlr::makeLearner">makeLearner</a></code>.

learner

[<code>character(1)</code>]
Classification: &#147;response&#148; or &#147;prob&#148;.
Regression: &#147;response&#148; or &#147;se&#148;.
Survival: &#147;response&#148; (linear predictor) or &#147;prob&#148;.
Clustering: &#147;response&#148; or &#147;prob&#148;.
Default is &#147;response&#148;.

predict.type


Possible prediction types are:
Classification: Labels or class probabilities (including labels).
Regression: Numeric or response or standard errors (including numeric response).
Survival: Linear predictor or survival probability.<p></p><p>For complex wrappers the predict type is usually also passed down the
encapsulated learner in a recursive fashion.
</p>

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.

Bernd Bischl

Machine Learning in R

setPredictType function

[<code><a rd-options='' href='Learner'>Learner</a></code> | <code>character(1)</code>]
The learner.
If you pass a string the learner will be created via <code><a rd-options='' href='makeLearner'>makeLearner</a></code>.


Possible prediction types are:
Classification: Labels or class probabilities (including labels).
Regression: Numeric or response or standard errors (including numeric response).
Survival: Linear predictor or survival probability.

For complex wrappers the predict type is usually also passed down the
encapsulated learner in a recursive fashion.


setPredictType: Set the type of predictions the learner should return.

Description

Usage

Arguments

Value

See Also