Reduces the dimensionality of the data by projecting
it onto a lower dimensional subspace using a random
matrix with columns of unit length (It will reduce
the number of attributes in the data while preserving
much of its variation like PCA, but at a much less
computational cost).
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from multiple classifiers based
on a given random number seed.
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the
splitting criterion based on residuals of the LogitBoost algorithm.
This class implements the statistics functions used in the
propositional rule learner, from the simpler ones like count of
true/false positive/negatives, filter data based on the ruleset, etc.
The description length (DL) of the ruleset relative to if the
rule in the given position is deleted, which is obtained by:
MDL if the rule exists - MDL if the rule does not exist
Note the minimal possible DL of the ruleset is calculated(i.e. some
other rules may also be deleted) instead of the DL of the current
ruleset.