Abstract: An automatic multi-threshold feature filtering method and an apparatus thereof are provided. In an iterative process of training a machine learning model, the feature filtering method calculates a feature filtering threshold and feature correlation values of a current round of iteration based on a result of a previous iteration, and performs feature filtering on samples based on the calculated feature filtering threshold and the calculated feature correlation values. The feature filtering apparatus of the present disclosure includes a calculation module and a feature filtering module. The method and apparatus of the present disclosure can automatically generate different feature filtering thresholds at each iteration, which greatly improves an accuracy of a filtering threshold, and can greatly increase the training speed of automatic machine learning and an accuracy of a machine learning model compared with fixed and single thresholds nowadays.

Abstract: An automatic multi-threshold feature filtering method and an apparatus thereof are provided. In an iterative process of training a machine learning model, the feature filtering method calculates a feature filtering threshold and feature correlation values of a current round of iteration based on a result of a previous iteration, and performs feature filtering on samples based on the calculated feature filtering threshold and the calculated feature correlation values. The feature filtering apparatus of the present disclosure includes a calculation module and a feature filtering module. The method and apparatus of the present disclosure can automatically generate different feature filtering thresholds at each iteration, which greatly improves an accuracy of a filtering threshold, and can greatly increase the training speed of automatic machine learning and an accuracy of a machine learning model compared with fixed and single thresholds nowadays.