Predictive machine learning feature selection

A system comprising:

• one or more processors;
• and
• at least one memory storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to:
• generate a model description vector from a textual model description for a target machine learning model;
• generate a plurality of description-based similarity vectors based on the model description vector, wherein:
• (i) a description-based similarity vector of the plurality of description-based similarity vectors comprises one or more of a plurality of similarity scores for a plurality of machine learning features, and
• (ii) the plurality of similarity scores is based on the model description vector;
• generate a label-based similarity vector based on a comparison between training data for the target machine learning model and a plurality of feature values of the plurality of machine learning features;
• generate, based on the plurality of description-based similarity vectors and the label-based similarity vector, a predictive feature set comprising a pre-defined number of features that provide improved model performance when training the target machine learning model;
• and
• train the target machine learning model using the predictive feature set.

One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:

• generate a model description vector from a textual model description for a target machine learning model;
• generate a plurality of description-based similarity vectors based on the model description vector, wherein:
• (i) a description-based similarity vector of the plurality of description-based similarity vectors comprises one or more of a plurality of similarity scores for a plurality of machine learning features, and
• (ii) the plurality of similarity scores is based on the model description vector;
• generate a label-based similarity vector based on a comparison between training data for the target machine learning model and a plurality of feature values of the plurality of machine learning features;
• generate, based on the plurality of description-based similarity vectors and the label-based similarity vector, a predictive feature set comprising a pre-defined number of features that provide improved model performance when training the target machine learning model;
• and
• train the target machine learning model using the predictive feature set.