Systems and methods of implementing scorecards and boosted decision trees

Abstract

Systems and methods are described for machine learning-based generation of scorecards and boosted decision trees that facilitate explainable predictions. A scorecard machine learning model may be applied to historical records such that the model, for each of a number of variables, automatically generates (a) normal bins for normal values of the variable that fall within a valid range of values and (b) at least one special bin for special values of the variable that fall outside the valid range of values. Adjacent bins of the normal bins may be separated by a threshold value and each normal bin and each special bin may have an assigned score value. A risk assessment score may be generated based at least in part on the model identifying the assigned value score for each of the variables based on the normal or special bin to which each variable is assigned.

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