Explanatory dropout for machine learning models

An improved computer implemented machine learning system having one or more processors configured for executing instructions to perform operations comprising:

• processing a machine learning model, the machine learning model trained using a plurality of input examples provided to the machine learning model, by stochastically applying one or more dropout operators to one or more input examples, the training being based on conditions including an absence or a presence of the one or more dropout operators applied to the one or more input examples;
• evaluating the machine learning model under the conditions of absence and presence of the one or more dropout operators applied to the one or more input examples;
• computing explanatory statistics for the machine learning model based on the machine learning model's output generated under the conditions of absence and presence of one or more dropout operators applied to the one or more input examples, and
• generating one or more reason codes associated with the one or more dropout operators, wherein the one or more reason codes provide an explanation for how the machine learning model behaves when the one or more dropout operators are present or absent.

A computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:

• processing a machine learning model, the machine learning model trained using a plurality of input examples provided to the machine learning model, by stochastically applying one or more dropout operators to one or more input examples, the training being based on conditions including an absence or a presence of the one or more dropout operators applied to the one or more input examples;
• evaluating the machine learning model under the conditions of absence and presence of the one or more dropout operators applied to the one or more input examples;
• computing explanatory statistics for the machine learning model based on the machine learning model's output generated under the conditions of absence and presence of one or more dropout operators applied to the one or more input examples, and
• generating one or more reason codes associated with the one or more dropout operators, wherein the one or more reason codes provide an explanation for how the machine learning model behaves when the one or more dropout operators are present or absent.