User consumption behavior prediction model training method, device, equipment and storage medium

Abstract

The application discloses a user consumption behavior prediction model training method, device, equipment and storage medium, and relates to the technical field of machine learning model training. Obtain the training set and test set from the database; obtain the prediction model initialization weight vector, inverse connection function and learning rate parameters; for any training data, normalize the weight vector and construct the first random variable, the first random variable is more The first inner product estimate is obtained by subsampling, and the weight vector is updated by the inverse connection function, the first inner product estimate, the label information of the training data, and the learning rate parameter; each weight vector is tested with test data, and the risk of the prediction model is minimized The weight vector of the user's consumption behavior prediction model after training is obtained. During the training process of the generalized linear model, the inner product estimation is approximated by sampling random variables multiple times to improve the efficiency of model training and ensure the accuracy of the model, so that the generalized linear model can effectively predict user consumption behavior.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to the technical field of machine learning model training. Background technique [0002] Generalized linear model (GLM) is a flexible linear regression model, which is a very basic and widely used method in machine learning. The generalized linear model establishes the relationship between the mathematical expectation value of the random variable measured by the experimenter and the predictor variable of the linear combination through the link function. Its model assumes that the output y and each input vector x have a linear relationship after acting on the link function, that is, y=g(w x), where g is the inverse link function (the inverse of the link function), w x is the inner product of the weight vector w and the input vector x. The core of the technical problem of generalized linear model learning is to design an efficient scheme to learn the weight vector w from...

AI Technical Summary

Problems solved by technology

[0004] The existing sparse sub-method runs slowly, seriously affecting the training efficiency of the generalized linear model, especially when the generalized linear model is used to predict user consumption behavior, due to the high dimensionality of the input data, the training speed is slow and the training process takes a long time; However, the sample complexity of the GLMtron method is not optimal. To achieve the same accuracy, more samples are required than the sparse sub-method, that is, a larger training set.

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