Machine learning model optimization effect evaluation method and device, terminal and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, and provides a machine learning model optimization effect evaluation method and device, a terminal and a storage medium, and the method comprises the steps: segmenting a historical sample data set into a plurality of test sample data sets; predicting the plurality of test sample data sets by using a plurality of machine learning models to obtain a plurality of prediction values; constructing an evaluation function based on the business indexes and the technical indexes, and calculating a plurality of evaluation scores according to the plurality of prediction values and the evaluation function; constructing an evaluation matrix based on the plurality of evaluation scores; and evaluating an optimization effect value ofeach machine learning model according to a preset optimization effect evaluation model and the evaluation matrix. Each machine learning model subjected to multi-iteration optimization can be evaluatedby combining the business indexes and the technical indexes.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a method, device, terminal and storage medium for evaluating the optimization effect of a machine learning model. Background technique [0002] With the rapid development of machine learning, more and more business scenarios use machine learning models for prediction. For example, in insurance business scenarios, machine learning models are used to predict user retention rates. [0003] In the prior art, multiple iterative optimizations are performed on the machine learning model and the latest optimized model is selected for prediction. However, the inventor found in the process of realizing the present invention that the prediction accuracy of the latest optimization model must be higher than the prediction accuracy of the historical optimization model, and although the machine learning model can predict a certain sample, it may It takes a long time to...

AI Technical Summary

Problems solved by technology

However, the inventor found in the process of realizing the present invention that the prediction accuracy of the latest optimization model must be higher than the prediction accuracy of the historical optimization model, and although the machine learning model can predict a certain sample, it may It takes a long time to determine whether the prediction result of the machine learning model is accurate for this sample

Images