PERSPECTIVE ANALYSIS METHOD FOR MACHINE LEARNING MODEL

摘要

A perspective analysis method for a machine learning model, comprising: collecting error data fed back by a user and extracting basic information, and extracting related information in the feedback data to generate a feature spatial vector; calculating a score of the query result and using an original model and submodels to learn and classify the user query result to obtain the classification result, i.e., an evaluation score; and for each user query, calculating an nDCG value of the query result, obtaining the actual sorting according to the training result of the machine learning model, and obtaining an ideal sorting according to the query result and the user query. The method comprises analyzing the submodels in a learning model, filtering the submodels having bad classification results, selecting the submodels having good classification results, and regrouping the selected submodels to generate a new learning model. The generated new learning model has higher prediction accuracy.