Post-modeling visualization

A computer program product comprising:

• one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising:
• program instructions to select a top N features for a machine learning (ML) model trained on training data;
• program instructions to discretize values of each continuous feature of the top N features into a set of categories;
• program instructions to generate a set of combination values that each represent a unique combination of feature values in a row representing a record within the training data;
• program instructions to predict, using the ML model, a target value for each record within the training data generating predicted target values;
• program instructions to group the predicted target values based on the combination value for each respective record of the training data;
• program instructions to fit a distribution for each grouping of the predicted target values associated with a respective combination value generating a set of distributions and associated distribution curves;
• program instructions to cluster and refit the set of distributions using a clustering algorithm to compress a number of distributions resulting in a set of clusters and a refitted distribution for each cluster of the set of clusters, wherein each refitted distribution is based on records associated with each distribution of the associated cluster;
• program instructions to assign a different color to each feature of the top N features and a different shade of the respective different color for each category of the set of categories for each respective feature;
• and
• program instructions to output a visualization of (1) the refitted distribution for each cluster as a distribution curve on a graph and (2) the associated records of the top N features as a table.

A computer system comprising:

• one or more computer processors;
• one or more computer readable storage media;
• program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising:
• program instructions to select a top N features for a machine learning (ML) model trained on training data;
• program instructions to discretize values of each continuous feature of the top N features into a set of categories;
• program instructions to generate a set of combination values that each represent a unique combination of feature values in a row representing a record within the training data;
• program instructions to predict, using the ML model, a target value for each record within the training data generating predicted target values;
• program instructions to group the predicted target values based on the combination value for each respective record of the training data;
• program instructions to fit a distribution for each grouping of the predicted target values associated with a respective combination value generating a set of distributions and associated distribution curves;
• program instructions to cluster and refit the set of distributions using a clustering algorithm to compress a number of distributions resulting in a set of clusters and a refitted distribution for each cluster of the set of clusters, wherein each refitted distribution is based on records associated with each distribution of the associated cluster;
• program instructions to assign a different color to each feature of the top N features and a different shade of the respective different color for each category of the set of categories for each respective feature;
• and
• program instructions to output a visualization of (1) the refitted distribution for each cluster as a distribution curve on a graph and (2) the associated records of the top N features as a table.