Multi-model fusion method

Abstract

The invention discloses a multi-model fusion method, 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: acquring and processing a data set containing user characteristics; based on an XGBoost algorithm and two different Y value intersection interval settings, selecting different derivation methods to derive characteristics in the processed data set; inputting the data set features and all the derived features into two XGBoost models as a new data set for training and optimization, and outputting the leaf node number of each training set sample at each tree by the two XGBoost models after optimization; Training One-hot coding according to leaf node number set, and enabling the leaf node number output by each XGBoost model to be converted by the trained One-hot code 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 obtaining an XGBoost + LR fusion model. The method is used for classifying users.

Description

technical field [0001] A multi-model fusion method is used for classifying users 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, learn from it, and then make a decision or prediction about ...

AI Technical Summary

Problems solved by technology

[0009] In view of the above-mentioned research problems, the purpose of the present invention is to provide a multi-model fusion method to solve the problems of traditional model classification with few dimensions, low precision, complex iterations, inability to handle data sparsity and weak univariate discrimination; The correlation between the features and labels obtained by the first-level model in the existing multi-modal fusion is strong, which does not help the subsequent model training, and also results in low generalization ability, and is biased towards the weight consideration of the first-level model. Reference to the weights of each model

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