| CPC G06F 11/3447 (2013.01) [G06F 18/23 (2023.01)] | 25 Claims |
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1. A computer-implemented method for
exploring predictive models, the method comprising:
computing feature importance for each of a plurality of features for each of a plurality of predictive models;
clustering predictive models among the plurality of predictive models to form a plurality of model clusters based on a similarity of feature importance of the computed feature importance for each of the plurality of features for each of the plurality of predictive models;
deriving a cluster feature importance for each model cluster of the plurality of model clusters;
computing a feature importance for a data case using a preliminary model;
selecting a model cluster for the data case from the plurality of model clusters based on similarity between the feature importance for the data case and the cluster feature importance of each model cluster of the plurality of model clusters; and
selecting a predictive model within the selected model cluster to score the data case based on at least one criteria thereby optimizing an evaluation of the plurality of predictive models among a massive set of predictive models.
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