Method and device for training tree model and device for training tree model

Abstract

The embodiment of the invention provides a method and device for training a tree model and a device for training the tree model, the method and device are used for training the tree model based on a data set, the data set comprises m pieces of sample data and m sample labels, each piece of sample data comprises n features, and the features and feature values in the data set are ciphertext. The method comprises the following steps: generating candidate groups based on a ciphertext according to a data set; dividing the data set into a left subset and a right subset based on the ciphertext according to each candidate group; calculating a division coefficient of each candidate group based on the left subset and the right subset obtained by dividing each candidate group; determining a feature in the target candidate group as an optimal feature, determining a threshold in the target candidate group as an optimal segmentation point, and determining the optimal feature and the optimal segmentation point as ciphertext; and distributing the data set to two sub-nodes of the current node according to the optimal feature and the optimal segmentation point. According to the embodiment of the invention, the tree model can be trained based on the ciphertext of the data to protect the privacy security of the data.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for training a tree model and a device for training the tree model. Background technique [0002] A decision tree is a tree structure. Each internal node in the tree represents a judgment on an attribute, each branch represents the output of a judgment result, and each leaf node represents a classification result. Decision trees can be trained with sample data. Using the trained decision tree, it can give correct classification results for new data. [0003] With the advent of the era of big data, the business data generated by users in the process of using network services is collected under the big data platform, which inevitably contains sensitive information related to user identity confidentiality, account security and personal privacy. Leakage can bring serious harm to users' lives. [0004] Therefore, how to protect the privacy and security of data...

AI Technical Summary

Problems solved by technology

[0003] With the advent of the era of big data, the business data generated by users in the process of using network services is collected under the big data platform, which inevitably contains sensitive information related to user identity confidentiality, account security and personal privacy. Leakage can cause serious harm to users' lives

Images