regr.featureless

A very basic baseline method which is useful for model comparisons (if you
don&#39;t beat this, you very likely have a problem).
Does not consider any features of the task and only uses the target feature
of the training data to make predictions.
Using observation weights is currently not supported. Methods &#8220;mean&#8221; and &#8220;median&#8221; always predict a constant value
for each new observation which corresponds to the observed mean or median of
the target feature in training data, respectively. The default method is &#8220;mean&#8221; which corresponds to the ZeroR algorithm
from WEKA, see <a href="https://weka.wikispaces.com/ZeroR">https://weka.wikispaces.com/ZeroR</a>.

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.

regr.featureless: Featureless regression learner.

Description

Arguments