Class Level Feature Importance Using Lasso and Probabilistic Graphical Models

A system for binning features comprising a computer, the computer comprising a processor and memory and configured to:

• obtain a frequency of values of features;
• calculate a number of records for each bin;
• iterate through a set of tuples;
• add a feature value to a bin list by determining if a temporary count is greater than or equal to a bin size;
• increment the temporary count in response to determining that the temporary count is greater or equal to bin size;
• and
• in response to determining that the temporary count is greater than zero, return a bin list.

A non-transitory computer-readable storage medium embodied with software for binning features, the software when executed being configured to:

• obtain, by a computer comprising a processor and memory, a frequency of values of features;
• calculate, by the computer, a number of records for each bin;
• iterate, by the computer, through a set of tuples;
• add, by the computer, a feature value to a bin list by determining if a temporary count is greater than or equal to a bin size;
• increment, by the computer, the temporary count in response to determining that the temporary count is greater or equal to bin size;
• and
• in response to determining that the temporary count is greater than zero, return, by the computer, a bin list.