Computing system and method for applying Monte Carlo estimation to determine the contribution of individual input variables within dependent variable groups on the output of a data science model

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 model object for a data science model using a machine learning process, wherein the model object is trained to (i) receive an input data record comprising a set of input variables and (ii) output a score for the input data record;
• arrange the set of input variables into two or more variable groups based on dependencies between respective input variables, where each variable group comprises at least one input variable;
• identify a given input data record to be scored by the model object;
• for each respective variable group of the model object, determine a contribution value for the respective variable group;
• for each respective input variable in each respective variable group of the model object, perform a given number of iterations of:
• identify a sample historical data record from a set of historical data records;
• determine a first model object output score for the sample historical data record;
• use the given input data record to determine a second model object output score for the sample historical data record that is conditioned on the respective variable group in the given input data record;
• select a random variable coalition, within the respective variable group, comprising the respective input variable and zero or more other input variables;
• use the given input data record, the sample historical data record, and the randomly-selected variable coalition to compute a group-specific contribution value for the respective input variable in the respective variable group;
• and
• use (i) the contribution value for the respective variable group, (ii) the first model object output score, (iii) the second model object output score, and (iv) the group-specific contribution value for the respective input variable to compute an iteration-specific contribution value for the respective input variable to the model output;
• and
• for each respective input variable of the model object, aggregate the iteration-specific contribution values calculated for each iteration and thereby determine an aggregated contribution value for the respective input variable.

A method carried out by a computing platform, the method comprising:

• training a model object for a data science model using a machine learning process, wherein the model object is trained to (i) receive an input data record comprising a set of input variables and (ii) output a score for the input data record;
• arranging the set of input variables into two or more variable groups based on dependencies between respective input variables, where each variable group comprises at least one input variable;
• identifying a given input data record to be scored by the model object;
• for each respective variable group of the model object, determining a contribution value for the respective variable group;
• for each respective input variable in each respective variable group of the model object, performing a given number of iterations of:
• identifying a sample historical data record from a set of historical data records;
• determining a first model object output score for the sample historical data record;
• using the given input data record to determine a second model object output score for the sample historical data record that is conditioned on the respective variable group in the given input data record;
• selecting a random variable coalition, within the respective variable group, comprising the respective input variable and zero or more other input variables;
• using the given input data record, the sample historical data record, and the randomly-selected variable coalition to compute a group-specific contribution value for the respective input variable in the respective variable group;
• and
• using (i) the contribution value for the respective variable group, (ii) the first model object output score, (iii) the second model object output score, and (iv) the group-specific contribution value for the respective input variable to compute an iteration-specific contribution value for the respective input variable to the model output;
• and
• for each respective input variable of the model object, aggregating the iteration-specific contribution values calculated for each iteration and thereby determine an aggregated contribution value for the respective input variable.