A method and apparatus for training a prediction model for a target scene

Abstract

Disclosed are a prediction model training method and device for a target scene. A prediction model training method for target scene includes obtaining N source training sample sets and one target training sample set; an iterative process is performed using the following steps until a preset end condition is reached: for each sample set of N source training sample sets, the sample set is merged with a target training sample set; using the merged sample set, the candidate model is trained. Each sample in the target training sample set is inputted into an alternative model, and the prediction error of the model is calculated according to the prediction value outputted from the model and the label value of each sample. The model with the smallest prediction error among the N candidate models is selected as the optimal model obtained by this iteration. After the iteration, according to the preset screening rules, all or part of the selected models are selected to be weighted from each iteration to obtain the prediction model for the target scenario.

Description

technical field [0001] The embodiments of this specification relate to the field of Internet application technology, and in particular to a method and device for training a prediction model for a target scene. Background technique [0002] In the era of big data, based on the accumulated samples, the model can be trained through machine learning to achieve the required prediction function. For example, in the financial risk control scenario, a large amount of transaction data can be used as a sample, and the risk control model can be trained through machine learning, so that the trained risk control model can be used to automatically predict the risk of new transactions. [0003] However, in some scenarios, it takes a long time to accumulate samples and train the model, so it is impossible to accumulate enough samples in a short period of time, so that a model with better prediction effect cannot be trained. [0004] One solution to this is to deploy a historical model in t...

AI Technical Summary

Problems solved by technology

[0003] However, in some scenarios, it takes a long time to accumulate samples and train the model, so it is impossible to accumulate enough samples in the short term to train a model with better prediction effect

[0004] One solution to this is to deploy a historical model in the target scene, which is trained based on historical samples from several other source The effect in the scene is usually poor

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