Classifier construction method, and method of prediction classification

Abstract

The invention provides a classifier construction method, and a method of prediction classification. The classifier construction method includes the steps: acquiring a training data set of a pluralityof training samples, wherein the training data set includes attribute information and classification information; extracting the attribute characteristic from the attribute information; taking the attribute characteristic as an initial independent variable, and taking the corresponding classification information as an initial dependent variable to perform at least one round of model training, wherein at least candidate models take part in each round of model training; and taking the combination with the minimum error rate in the each round of model training as a classifier which has completedtraining. The classifier construction method, and the method of prediction classification perform at least one round of model training on the basis of at least two candidate models to obtain a classifier and can predict classification of target samples according to the trained classifier, thus being able to avoid the problem that a single classification method is low in prediction precision and prediction accuracy, and being both higher in precision and accuracy of prediction.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a method for constructing a classifier and a method for predicting classification. Background technique [0002] The classification algorithm is based on the classification model to select the best category hypothesis for the sample to be tested from the optional classifications. For example, the loan classification model can classify a customer's borrowing intention on the lending platform as possible or impossible. [0003] Corresponding to the above classification model, it mainly includes the model training phase, the model verification phase and the model application phase. Among them, the above-mentioned model training stage is to establish a model and use historical data sets to establish a classification model. The model verification stage is to verify the above-mentioned models established based on historical data sets, such as using cross-validation methods fo...

AI Technical Summary

Problems solved by technology

However, the above classification algorithms all use a single classification method. When applied to actual business data, due to the limitations of the algorithm itself, a good prediction accuracy and prediction accuracy cannot be achieved.

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