For kernel classification models, the raw classification
score for classifying the observation x, a row vector,
into the positive class is defined by
is a transformation of an observation for feature
expansion.
β is the estimated column vector of coefficients.
b is the estimated scalar bias.
The raw classification score for classifying x into the negative class is −f(x). The software classifies observations into the class that yields a
positive score.
If the kernel classification model consists of logistic regression learners, then the
software applies the 'logit'
score transformation to the raw
classification scores (see ScoreTransform
).