Machine learning framework for generating multiple machine-learned models based on different sets of training data

One or more storage media storing instructions which, when executed by one or more computing devices, cause:

• generating a plurality of sets of artificial feature values, wherein generating the plurality of sets of artificial feature values comprises:
• identifying a set of feature values in a plurality of sets of feature values;
• shuffling the set of feature values to generate a set of artificial feature values, wherein the set of artificial feature values is included in the plurality of sets of artificial feature values;
• for each set of artificial feature values of the plurality of sets of artificial feature values:
• generating a training data set based on said each set of artificial feature values and the plurality of sets of feature values;
• wherein each set of feature values in the plurality of sets of feature values corresponds to a different feature of a plurality of features;
• generating a machine-learned model based on the training dataset;
• based on the machine-learned model, generating a ranking of the plurality of features;
• adding the machine-learned model to a set of machine-learned models;
• storing the ranking in a dictionary, of a plurality of dictionaries, that corresponds to said each set of artificial feature values;
• for each feature of the plurality of features:
• for each dictionary of the plurality of dictionaries:
• generating a rank pair comprising (1) a rank of said each feature, in said each dictionary, relative to other features of the plurality of features and (2) a rank of an artificial feature, in said each dictionary, relative to other features of the plurality of features;
• adding the rank pair to a set of rank pairs associated with said each feature;
• wherein after generating the rank pair for each dictionary of the plurality of dictionaries, the set of rank pairs is a plurality of rank pairs associated with said each feature;
• based on the plurality of rank pairs associated with said each feature, determining whether there is significant correlation between said each feature and a response variable of the set of machine-learned models.