Abstract: Provided are computing systems, methods, and platforms that automatically investigate and analyze the impact of new features or signals on the performance of a machine learning model by producing a ranked list of the most impactful features from input of a set of candidate features. In particular, one example computing system can import a training dataset associated with a user. The computing system can train a machine learning model for the training dataset and generate baseline metrics for the machine learning model. Correlations between features or signals in the training dataset can be identified and the features or signals can be grouped into clusters based on the correlations. The computing system can determine the importance of each cluster and each feature or signal. A ranked list of signals and their importances can be exported in decreasing order of machine learning model performance lift based on cluster importance and signal importance.

Abstract: Provided are computing systems, methods, and platforms that automatically investigate and analyze the impact of new features or signals on the performance of a machine learning model by producing a ranked list of the most impactful features from input of a set of candidate features. In particular, one example computing system can import a training dataset associated with a user. The computing system can train a machine learning model for the training dataset and generate baseline metrics for the machine learning model. Correlations between features or signals in the training dataset can be identified and the features or signals can be grouped into clusters based on the correlations. The computing system can determine the importance of each cluster and each feature or signal. A ranked list of signals and their importances can be exported in decreasing order of machine learning model performance lift based on cluster importance and signal importance.