System and method for utilizing grouped partial dependence plots and shapley additive explanations in the generation of adverse action reason codes

A non-transitory computer-readable medium, wherein the non-transitory computer-readable medium is provisioned with program instructions that, when executed by at least one processor, cause a computing platform to:

• train a machine learning model by carrying out a machine learning process on a training dataset comprising a set of input vectors and a corresponding set of output values, wherein the trained machine learning model is configured to (i) receive an input vector comprising respective values for a given set of input variables and (ii) based on an evaluation of the received input vector, generate and output a prediction;
• based on an evaluation of correlations between input variables in the given set of input variables of the trained machine learning model, divide the given set of input variables into a plurality of variable groups, wherein each variable group includes at least one input variable from the given set of input variables and each of at least a subset of the variable groups includes multiple input variables from the given set of input variables that are determined to have a threshold level of correlation with one another;
• after training the machine learning model and dividing the given set of input variables into the plurality of variable groups:
• receive a given input vector comprising respective values for the given set of input variables;
• process the input vector using the trained machine learning model to generate and output a given prediction corresponding to the given input vector;
• use a model interpretability technique based on game theory to determine, for each respective variable group in the plurality of variable groups, a respective Shapley-based value that quantifies a respective contribution of the respective variable group to the given prediction for the given input vector;
• and
• based on the respective Shapley-based values determined for the respective variable groups in the plurality of variable groups, identify which one or more input variables of the trained machine learning model contributed most to the given prediction.

A computer-implemented method comprising:

• training a machine learning model by carrying out a machine learning process on a training dataset comprising a set of input vectors and a corresponding set of output values, wherein the trained machine learning model is configured to (i) receive an input vector comprising respective values for a given set of input variables and (ii) based on an evaluation of the received input vector, generate and output a prediction;
• based on an evaluation of correlations between input variables in the given set of input variables of the trained machine learning model, dividing the given set of input variables into a plurality of variable groups, wherein each variable group includes at least one input variable from the given set of input variables and each of at least a subset of the variable groups includes multiple input variables from the given set of input variables that are determined to have a threshold level of correlation with one another;
• after training the machine learning model and dividing the given set of input variables into the plurality of variable groups:
• receiving a given input vector comprising respective values for the given set of input variables;
• processing the input vector using the trained machine learning model to generate and output a given prediction corresponding to the given input vector;
• using a model interpretability technique based on game theory to determine, for each respective variable group in the plurality of variable groups, a respective Shapley-based value that quantifies a respective contribution of the respective variable group to the given prediction for the given input vector;
• and
• based on the respective Shapley-based values determined for the respective variable groups in the plurality of variable groups, identifying which one or more input variables of the trained machine learning model contributed most to the given prediction.