Data prediction method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a data prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining feature information of to-be-predicted data and scene information of the to-be-predicted data, inputting the acquired information into a prediction model; obtaining prediction results, wherein the prediction model is obtained by training the following steps: acquiring scene information of the sample data, feature information of the sample data and classification types of the sample data; and inputting the scene informationof the sample data and the feature information of the sample data into a to-be-trained FFM model to obtain a first numerical value corresponding to the sample data, determining parameters of the to-be-trained FFM model according to the first numerical value and the classification category of the sample data, and determining a prediction model according to the to-be-trained FFM model after the parameters are determined. According to the embodiment of the invention, the data of different scenes can be predicted by training one model, and the models do not need to be trained for multiple scenes respectively, so that the model training process is simplified, the model training efficiency is improved, and the data prediction efficiency is higher.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a data prediction method, device, electronic equipment and storage medium. Background technique [0002] With the rapid development of computer technology, it is more and more widely used to predict data with trained models. Since different data may correspond to multiple different scenarios, when using a trained model to predict data, the trained model should be able to predict the data corresponding to different scenarios. For example, to predict whether a user will pay overdue, the user's loan methods include: commercial loan, provident fund loan, and combined loan. These three loan methods represent three different scenarios. The trained model should be able to predict each Predict whether the user of the loan method will be overdue in repayment. [0003] For the data corresponding to different scenarios, the prior art usually trains the model according to the fol...

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Problems solved by technology

[0004] However, the inventor found in the process of implementing the present invention that at least the following problems exist in the prior art: since the prior art trains a model for each scene, when there are many scenes, it takes more time to train the model, so that The process of model training is complicated, and the efficiency of model training is low, which makes the efficiency of data prediction low

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