Model prediction method, device and equipment and storage medium

Abstract

The invention discloses a model prediction method and device, equipment and a storage medium, the model prediction method comprises the following steps: obtaining prediction models of N stages, the prediction models of different stages being obtained based on different data source training, and N being an integer greater than 1; predicting the sample according to the prediction model of each stage; and if the sample is predicted according to the prediction model of the kth stage, according to the prediction result of the prediction model of the kth stage on the sample, screening out the sampleparticipating in the prediction of the next stage from the samples participating in the prediction of the current stage, and k being an arbitrary positive integer smaller than N. According to the embodiment of the invention, the data volume of calling the external data source can be reduced, and meanwhile, the prediction effect of the model can be ensured.

Description

technical field [0001] The invention belongs to the field of computers, and in particular relates to a model prediction method, device, equipment and storage medium. Background technique [0002] The current machine learning algorithm can accept thousand-dimensional feature input. These features come from different external data sources. Every time the prediction model is used for sample prediction, it is necessary to call all the data sources used at the same time, and the interface calls of different data sources. There is not only the time cost of calling, but also the corresponding data cost. [0003] In response to the above problems, the traditional approach is to reduce the original thousand-dimensional features to an acceptable range based on feature screening technology, which can ensure that the amount of data from external data sources is called as little as possible, but the cost is the prediction effect of the model The decline is more obvious. Contents of th...

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Problems solved by technology

[0003] In response to the above problems, the traditional approach is to reduce the original thousand-dimensional features to an acceptable range based on feature screening technology, which can ensure that the amount of data from external data sources is called as little as possible, but the cost is the prediction effect of the model The decline is more obvious

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