Training data resampling method and device, storage medium and electronic device

Abstract

The invention relates to a training data resampling method and device, a storage medium and an electronic device. The method comprises: obtaining first original data in a first time period; calculating a first proportion respectively occupied by a plurality of preset classifications in the first original data; according to the size relation of the first proportion, sorting the multiple preset classifications according to a preset rule to obtain a first sorting result; determining a sampling ratio corresponding to each preset classification according to the ranking of each preset classificationand a preset corresponding relationship, the preset corresponding relationship being a corresponding relationship between the ranking and the sampling ratio; and resampling the training data for modeling according to the sampling proportions respectively corresponding to the plurality of preset classifications. Thus, the problem that the classification model is unfriendly to small classes is solved, the classification accuracy of the classification model obtained through training of the training data for different applications is improved, and therefore the user experience is improved.

Description

technical field [0001] The present disclosure relates to the field of data mining, in particular, to a training data resampling method, device, storage medium and electronic equipment. Background technique [0002] In machine learning, the number of samples in the training data for different classifications in the classification model may often vary greatly. For example, in N training data, the number of samples belonging to the first class may be different from the number of samples belonging to the second class. The number of samples and the number of samples belonging to the third category are very different (for example, the number of samples belonging to the first category may account for 90% of the N training data, and the number of samples belonging to the second and third categories may only account for 90% of the N training data. 10% of the N training data), so that when the training data with an unbalanced number of samples is directly used to train the classificat...

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Problems solved by technology

[0003] The purpose of this disclosure is to provide a training data resampling method, device, storage medium and electronic equipment, which can resample the training data according to the proportion of different classifications in the actual original data in view of the unbalanced training data processing, so as to solve the problem that the classification model is not friendly to small categories

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