Machine learning model selection and explanation for multi-dimensional datasets

A method of interpreting a multi-dimensional dataset, the method comprising:

• applying a plurality of machine learning models to the multi-dimensional dataset to obtain a result output by each of the plurality of machine learning models;
• determining a correlation of the one or more dimensions of the multi-dimensional dataset to the results output by each of the plurality of machine learning models;
• selecting, based on the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models, a subset of the plurality of machine learning models to obtain the result for each of the subset of the plurality of machine learning models;
• and
• outputting the result for each of the subset of the plurality of machine learning models.

A non-transitory computer-readable storage medium storing instructions that, when executed, cause one or more processors to:

• apply a plurality of machine learning models to a multi-dimensional dataset to obtain a result output by each of the plurality of machine learning models;
• determine a correlation of the one or more dimensions of the multi-dimensional dataset to the result output by each of the plurality of machine learning models;
• select, based on the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models, a subset of the plurality of machine learning models to obtain the result for each of the subset of the plurality of machine learning models;
• and
• output the result for each of the subset of the plurality of machine learning models.