Multi-task prediction model training method and device and event type prediction method and device

Abstract

The embodiment of the invention provides a multi-task prediction model training and event type prediction method and device, and the method comprises the steps: obtaining an initial sample set in a training method; for any first sample, in the first i-1 decision trees, obtaining respective N scores of each leaf node including the first sample; and determining a plurality of gradients based on therespective N scores of each leaf node and the N label values of the first sample, and executing first fusion to obtain a fusion gradient of the first sample. And for the current node of the ith decision tree, obtaining the fusion gradient of each sample in the sample set segmented to the current node. And based on the fusion gradient of each sample in the current sample set, respectively determining a splitting feature and a feature threshold of the current node from each sample feature and the feature value of each sample in the current sample set corresponding to each sample feature. And segmenting the current sample set based on the splitting feature and the feature threshold of the current node, and generating a sub-node corresponding to the current node until the sub-node reaches theleaf node.

Description

technical field [0001] One or more embodiments of this specification relate to the field of computer technology, and in particular to a method and device for training a multi-task prediction model and predicting event types. Background technique [0002] In traditional techniques, Gradient boosted decision trees (GBDT) can usually only be used to train single-task prediction models. The single-task prediction model here means that it can only be predicted for a single task. For example, a single-task prediction model can only predict whether the current text is an advertisement, or whether the current event is a fraud event, etc. [0003] However, in real-world scenarios, multi-task prediction is usually required. For example, in order to achieve effective risk management and control of events, it is usually necessary to predict the probability corresponding to each type of fraud for an event. Based on this, it is necessary to provide a training method for a multi-task pr...

AI Technical Summary

Problems solved by technology

For example, a single-task prediction model can only predict whether the current text is an advertisement, or whether the current event is a fraud event, etc.

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