CPC G06N 20/20 (2019.01) [G06N 5/01 (2023.01); G06N 5/04 (2013.01)] | 17 Claims |
1. A method of generating a confidence score for a boosting-based tree machine learning model score comprising:
generating a plurality of bootstrap models using a dataset of a boosting-based tree machine learning model;
determining, for first media data comprising a first plurality of attributes, a plurality of output scores comprising a respective output score for each of the plurality of bootstrap models;
determining, for the first media data, a first standard deviation of the plurality of output scores;
training a confidence score prediction machine learning model to generate confidence scores using a training instance comprising the first standard deviation as a target label;
receiving second media data comprising a second plurality of attributes;
determining, by the boosting-based tree machine learning model for the second media data, a first class score, wherein the first class score indicates a prediction related to the second media data;
determining, by an outlier prediction machine learning model, an outlier prediction score for the second media data using the second plurality of attributes;
inputting the outlier prediction score and the first class score into the confidence score prediction machine learning model trained using the training instance;
generating, by the confidence score prediction machine learning model, a confidence score indicating a confidence in the first class score determined by the boosting-based tree machine learning model for the second media data;
selecting the second media data as recommended media data based on the confidence score being above a confidence score threshold; and
generating first output data comprising a recommendation of the second media data.
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