A user classification method based on multi-model fusion

Abstract

The invention discloses a user classification method based on multi-model fusion, belongs to the technical field of machine learning, and solves the problems of less dimension, low precision and complex iteration in traditional model classification. The method comprises the steps that a data set containing user characteristics is acquired and processed; the method is based on an XGBoost algorithmand two different Y value intersection interval settings, different derivation methods are selected to derive characteristics in the processed data set; the data set features and all the derived features are input into two XGBoost models as a new data set for training and optimization, and the leaf node number of each training set sample at each tree is output by the two XGBoost models after optimization; one-hot coding is trained according to leaf node number ser, and the leaf node number output by each XGBoost model is subjected to the trained One-hot code conversion to obtain all LR features corresponding to the training set samples, and training an optimization LR model based on the LR features corresponding to all the training set samples; and an XGBoost + LR fusion model is obtainedto classify the users. The method is used for classifying users based on multi-model fusion.

Description

technical field [0001] A user classification method based on multi-model fusion is used for classifying users based on multi-model fusion, and belongs to the technical field of machine learning. Background technique [0002] Machine learning is a multi-field interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. Specializes in the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills, and reorganize existing knowledge structures to continuously improve their performance. [0003] Machine learning is a subset of artificial intelligence and the core of artificial intelligence. It is the fundamental way to make computers intelligent. Its application pervades all fields of artificial intelligence. It mainly uses induction and synthesis rather than deduction. At its core, machine learning is "using algorithms to parse data, le...

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Problems solved by technology

[0009] In view of the above-mentioned research problems, the purpose of the present invention is to provide a user classification method based on multi-model fusion, which solves the problems of traditional model classification with few dimensions, low precision, complex iteration, inability to handle data sparsity well, and weak univariate discrimination. Problem; to solve the problem that the correlation between the features and labels obtained by the first-level model in the most existing multi-mode fusion is strong, which does not help the subsequent model training, and also results in low generalization ability, and is biased towards the first-level model. Weight considerations do not refer to the weight of each model, resulting in poor classification results

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