Compressing dataset using machine learning models

A computer system for generating a compressed dataset, comprising:

• a processor set;
• a set of one or more computer-readable storage media;
• and
• program instructions stored on the set of one or more computer-readable storage media to cause the processor set to perform operations comprising:
• training a first machine learning model using a dataset, wherein the first machine learning model is trained to generate probabilities of predictions based on a number of features extracted from columns in the dataset;
• ranking the number of features based on a feature attribution analysis for the number of features, wherein the feature attribution analysis determines contribution of each feature from the number of features to predictions made by the first machine learning model;
• and
• expanding a feature vector using a number of top ranked features from the number of features for generating the compressed dataset, wherein the compressed dataset comprises a number of data entries from the dataset that is associated with the number of top ranked features from the number of features.

A computer program product for generating a compressed dataset, comprising:

• a set of one or more computer-readable storage media;
• program instructions stored in the set of one or more computer-readable storage media to perform operations comprising:
• training, by a processor set, a first machine learning model using a dataset, wherein the first machine learning model is trained to generate probabilities of predictions based on a number of features extracted from columns in the dataset;
• ranking, by the processor set, the number of features based on a feature attribution analysis for the number of features, wherein the feature attribution analysis determines contribution of each feature from the number of features to predictions made by the first machine learning model;
• and
• expanding, by the processor set, a feature vector using a number of top ranked features from the number of features for generating the compressed dataset, wherein the compressed dataset comprises a number of data entries from the dataset that is associated with the number of top ranked features from the number of features.