Training method, device, storage medium and electronic equipment of random forest

Abstract

The present disclosure relates to a random forest training method, device, storage medium and electronic equipment. The method includes: determining n groups of training data sets in the first training data; n trees, obtain n prediction results; delete n trees according to the correct rate of n prediction results and the preset threshold value, and obtain m trees; Vote to obtain the target tree; synthesize the prediction result corresponding to the target tree and the description data into the second training data; use the second training data as the first training data, and execute the above steps in a loop until the number of n prediction results If the correct rate is greater than or equal to the preset threshold, a random forest is obtained. It can continuously optimize the overall training data during the multiple training process of the random forest, and improve the accuracy of classification prediction while avoiding the increase of trees with a single feature during the training process.

Description

technical field [0001] The present disclosure relates to the field of machine learning, in particular, to a random forest training method, device, storage medium and electronic equipment. Background technique [0002] A random forest is a classifier that contains multiple decision trees, and its output prediction is determined by the mode of the prediction output by each tree. The decision tree is a tree-structured model for supervised learning. In supervised learning, a set of samples can be given first, each sample contains a set of attributes (descriptive data) and a category (prediction result), these categories are determined in advance, by learning this set of samples can get a The decision tree of the classification function, which can give the correct classification (output prediction result) to the new object. In related technologies, when training a random forest, each decision tree in the random forest is usually trained once with a part of the full training dat...

AI Technical Summary

Problems solved by technology

However, the prediction accuracy rate of the decision tree that only undergoes a single training is not high, and it cannot cope with the unbalanced data characteristics in the training data during the training process (there is a lot of data in a certain category), which in turn reduces the accuracy of the entire classification prediction. question

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