RANKED FACTOR SELECTION FOR MACHINE LEARNING MODEL

    公开(公告)号:US20220222483A1

    公开(公告)日:2022-07-14

    申请号:US17576040

    申请日:2022-01-14

    Abstract: Described herein are techniques to a systematic approach to reduce the number of factors of an input dataset that impact a target prediction of a trained ML model. The techniques include obtaining a dataset of typed data points and ascertaining the factors of the data points based, at least in part, on the datatypes of the data points. The techniques also include obtaining an indicator of correlation of each factor ascertained in the dataset to a target prediction by a trained ML model and assigning a score to each respective factor ascertained in the dataset based on the indicator of correlation of each factor. The techniques further include ranking the factors ascertained in the dataset based on the score of each factor, selecting factors from the factors ascertained in the dataset, and providing the selected factors for making the target prediction by the trained ML model.

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