Visualization method and device of random forest model and storage medium

Abstract

The invention discloses a visualization method and device for a random forest model and a storage medium, and relates to the technical field of machine learning, and the method comprises the steps ofscreening a target training sample meeting a preset condition from a training sample set corresponding to each decision tree of the random forest model, so as to form a target training sample set forconstructing a classification tree; obtaining the variable importance degree of each characteristic variable in each decision tree, and carrying out descending sorting on all the characteristic variables according to the variable importance degrees; according to the target training sample set and all the feature variables after descending sorting, starting from a root node of the classification tree, optimal feature variables and optimal segmentation values corresponding to all the nodes in the classification tree are sequentially determined by taking the Gini coefficient as a splitting rule,so that the classification tree is constructed; and generating a tree-shaped visual graph corresponding to the classification tree and outputting the tree-shaped visual graph. According to the invention, the decision process of the random forest model can be visually displayed, and the interpretability of the model is improved.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a visualization method, device and storage medium of a random forest model. Background technique [0002] The random forest algorithm is an algorithm that uses the idea of ​​ensemble learning to integrate multiple decision trees. By generating several weak classifiers—decision trees, and using a bagging algorithm that randomly selects variables and samples, the voting results of the weak classifiers are Output as the final prediction result, so that a more reasonable classification decision boundary can be obtained, the overall error can be reduced, and a better classification effect can be achieved. It is now widely used in financial risk control and other fields, and has strong prediction stability and anti-oversimulation Compatibility. [0003] In the process of realizing the present invention, the inventor finds that there are at least the following problems in the ...

AI Technical Summary

Problems solved by technology

[0004] Because the random forest algorithm integrates weak classifiers into bags, which is similar to the black box algorithm to a certain extent, resulting in a greatly reduced interpretability of the output results. At present, there is no relevant technical solution to integrate the random forest classification results It is displayed throu

Images