Systems and methods for generating models for classifying imbalanced data

A system, comprising:

• one or more processors;
• and
• memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
• receive a request to provide a classification model for processing and classifying different data sets;
• obtain a sample data set, wherein the sample data set includes first data associated with a first characteristic and second data associated with a second characteristic;
• identify a set of classification algorithms and a set of sampling algorithms;
• generate different classification models for processing of the sample data set, wherein the different classification models are generated by combining different classification algorithms from the set of classification algorithms with different sampling algorithms from the set of sampling algorithms;
• process the sample data set through the different classification models to generate different sets of classifications;
• evaluate the different classification models based on the different sets of classifications and according to a set of metrics for evaluating performance of the different classification models;
• select the classification model, wherein the classification model is selected based on the set of metrics and the performance of the different classification models;
• and
• provide the classification model and a summary of the performance of the different classification models.

A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:

• receive a request to provide a classification model for processing and classifying different data sets;
• obtain a sample data set, wherein the sample data set includes first data associated with a first characteristic and second data associated with a second characteristic;
• identify a set of classification algorithms and a set of sampling algorithms;
• generate different classification models for processing of the sample data set, wherein the different classification models are generated by combining different classification algorithms from the set of classification algorithms with different sampling algorithms from the set of sampling algorithms;
• process the sample data set through the different classification models to generate different sets of classifications;
• evaluate the different classification models based on the different sets of classifications and according to a set of metrics for evaluating performance of the different classification models;
• select the classification model, wherein the classification model is selected based on the set of metrics and the performance of the different classification models;
• and
• provide the classification model and a summary of the performance of the different classification models.