Predict class labels of a validation set.
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A logical scalar. Toggles whether to print from
A character string. Select from "probability" or "majority".
See Details for the implication of this choice. Argument applies to
ExprsMachine object can only predict against an
ExprsModule object can only predict against an
ExprsMulti object. The validation set should never get modified
once separated from the training set. If the training set used to build
ExprsModule had a class missing (i.e., has an NA placeholder),
ExprsModule cannot predict the missing class. To learn how
this scenario gets handled, read more at
ExprsPredict objects store predictions in three slots:
@probability. The first slot
stores a "final decision" based on the class label with the maximum
predicted probability. The second slot stores a transformation
of the predicted probability for each class calculated by the inverse
of Platt scaling. The predicted probability gets returned by the
predict method called using the stored
To learn how these slots get used to calculate classifier performance,
read more at
At the moment,
ExprsEnsemble can only make predictions
ExprsMachine objects. Therefore, it can only predict
ExprsBinary objets. Predicting with ensembles poses
a unique challenge with regard to how to translate multiple
performance scores (one for each classifier in the ensemble) into
a single performance score (for the ensemble as a whole). For now,
predict method offers two options,
toggled with the argument
how. Regardless of the chosen
buildEnsemble begins by deploying each constituent
classifier on the validation set to yield a list of
how = "probability", this method will take the average
predicted class probability (i.e.,
@probability for each
ExprsPredict object (corresponding to each constituent
ExprsModel object). When
how = "majority", this method
will let the final decision from each returned
@pred) cast a single (all-or-nothing) vote.
Each subject gets assigned the class that received the most number
of votes (i.e., winner takes all). In both scenarios, ties get
broken randomly with equal weights given to each class.
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