NONLINEAR FEATURIZATION OF DECISION TREES FOR LINEAR REGRESSION MODELING

摘要

Nonlinear featurization of decision trees for linear regression modeling in the context of an on-line social network is described. A computer-implemented converter is provided that is capable of reading a decision tree structure that is included in the learning to rank algorithm and convert each path from root to a leaf into an s-expression. The s-expressions are used as additional features to train a logistic regression model.