Bias-variance tradeoff of soft decision trees

This paper focuses on the study of the error composition of a fuzzy
decision tree induction method recently proposed by the authors,
called soft decision trees. This error may be expressed as a sum of
three types of error: residual error, bias and variance. The paper
studies empirically the tradeoff between bias and variance in a soft
decision tree method and compares it with the tradeoff of classical
crisp regression and classification trees. The main conclusion is
that the reduced prediction variance of fuzzy trees is the main reason
for their improved performance with respect to crisp ones.