Visualized optimization processing method and device for random forest classification model

Abstract

Disclosed is a visualized optimization processing method for a random forest classification model. The method comprises: for a random forest classification model which has been constructed, estimating the degree of correlation between various decision trees of the random forest classification model via out-of-bag data; constructing a correlation matrix using the degree of correlation between various decision trees of the random forest classification model; according to the correlation matrix, by means of the dimension reduction technology, acquiring a visual pattern of the random forest classification model in a space with dimensions fewer than three; and according to the visualized pattern of the random forest classification model, conducting optimization processing on the random forest classification model, so that the upper limit of a second generalization error of the processed random forest classification model does not go beyond the upper limit of a first generalization error of the random forest classification model prior to processing. By means of the above-mentioned method, the present invention can reduce the number of decision trees in the random forest classification model and reduce the memory space required by the random forest classification model, and can also improve the prediction speed and accuracy at the same time.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a visual optimization processing method and device for a random forest classification model. Background technique [0002] Classification problems are one of the most fundamental tasks frequently encountered in statistics, data analysis, machine learning, and data mining research fields. The main goal of this task is to use the training data to build a predictive model (ie, a learning machine) with strong generalization ability, and ensemble learning has significant advantages in this regard. The basic idea of ​​ensemble learning is to use multiple learning machines to solve the same problem. Two prerequisites determine the feasibility of ensemble learning: one is that a single basic learning machine is effective, that is to say, the accuracy of a single basic learning machine should be greater than the probability of random guessing; the other is the difference between the ...

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Problems solved by technology

[0004] The inventors of the present application found in the long-term research and development that the prediction effect of random forest is significantly better than that of a single decision tree, but there are

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