Method, apparatus and device for merging model prediction values

Abstract

A method, an apparatus, and a device for merging model prediction values are disclosed. The method for merging model prediction values comprises: based on a given number of samples, binning a prediction value of an online prediction model and a prediction value of an off-line prediction model respectively according to a set binning method; according to a binning result, converting the first prediction value of each sample into a first interval feature corresponding to the interval in which the first prediction value is located, and converting the second prediction value of each sample into a second interval feature corresponding to the interval in which the second prediction value is located; and taking the first interval feature, the second interval feature and a sample tag correspondingto each sample to form converted sample data, and using the converted sample data to train the model, wherein the trained model is used to merge the prediction value of the online prediction model andthe prediction value of the off-line prediction model to obtain a final prediction value.

Description

technical field [0001] This specification relates to the technical field of machine learning, and in particular to a method, device and equipment for fusing model prediction values. Background technique [0002] Machine learning algorithm is a kind of algorithm that can automatically analyze and obtain laws from data, and use the laws to predict unknown data. It is widely used in many fields. [0003] In practical applications, it includes online prediction model and offline prediction model. Among them, the offline prediction model is usually implemented by timing tasks. Its advantage is that it can incorporate features with higher dimensions and use more complex algorithms to achieve more accurate predictions. effect; however, due to the large number of features and complex algorithms, the prediction process is usually time-consuming. Compared with the offline prediction model, the online prediction model can use lower-dimensional features and simpler algorithms to achiev...

AI Technical Summary

Problems solved by technology

[0003] In practical applications, it includes online prediction model and offline prediction model. Among them, the offline prediction model is usually implemented by timing tasks. Its advantage is that it can incorporate features with higher dimensions and use more complex algorithms to achieve more accurate predictions. effect; however, due to the large number of features and complex algorithms, the prediction process is usually time-consuming

Compared with the offline prediction model, the online prediction model can use lower-dimensional features and simpler algorithms to achieve more efficient prediction. The disadvantage is that the features are not rich enough and the accuracy is not high.

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