A prediction method and device based on a decision tree

Abstract

The invention discloses a decision-making tree based prediction method and device and relates to the field of data processing, and the method and the device can improve the visualization effect of a decision-making tree model and the decision-making tree model testing process. The decision-making tree based prediction method includes steps of generating a target training set by feature selection of data in a defective data set of prestored products according to feature attributes; training a training set by a decision-making tree algorithm to generate a decision-making tree, wherein the training set belongs to the target training set; compressing the decision-making tree to obtain a first decision-making tree; displaying the first decision-making tree by means of the visualization technology; selecting at least one test case from a test set to be sequentially input into the first decision-making tree for testing, and generating classification paths for the test cases; displaying the classification paths of the test cases in the first decision-making tree by means of the visualization technology. The decision-making tree based prediction method and device is applied to defect prediction of the products.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a prediction method and device based on a decision tree. Background technique [0002] The decision tree model is a common defect prediction model. When a product fails, it can help maintenance personnel quickly locate the type of defect, come up with a maintenance plan in time, and reduce maintenance costs. However, due to the scattered release of some inherent attributes of many products, the decision tree model trained will be extremely large, such as thousands of types of defective products and defective boards, and the number of defect types (predicted attributes) will also reach several. There are hundreds of kinds, so in the face of such a huge decision tree, it is not feasible to use the traditional decision tree visualization method. Therefore, the prior art proposes a CABRO large-scale decision tree visualization technology. There are two left and right windows in the CA...

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Problems solved by technology

[0003] However, the inventors have found that the CABRO visualization system visualization technology in the prior art has the following problems: the CABRO visualization system interface cannot intuitively display the predicted path, and it is necessary to move the rectangular frame in the global window multiple times, and then watch it in the local window, so that the decision-making Tree model visualization is poor

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