Decision tree generation method and device, computer readable storage medium and electronic equipment

Abstract

The invention relates to a decision tree generation method and device, a computer readable storage medium and electronic equipment. The method comprises the steps that a sample data set is acquired, and the sample data set comprises an original feature set and a target column; Performing data preprocessing on the original feature set to form a target feature set; Respectively calculating a first correlation degree between each feature in the target feature set and the target column; And generating a decision tree based on a first correlation degree between each feature in the target feature set and the target column. Therefore, the decision tree generation method can be suitable for both discrete variables and continuous variables, namely, the decision tree generation method can be used for processing classification problems and regression problems. In addition, invalid feature filtering is not needed in the generation process of the decision tree, and the performance of the decision tree can be ensured.

Description

technical field [0001] The present disclosure relates to the field of machine learning, and in particular, relates to a decision tree generation method, device, computer-readable storage medium, and electronic equipment. Background technique [0002] Decision Tree (Decision Tree) is a decision analysis method for evaluating project risk and judging its feasibility by forming a decision tree to obtain the probability that the expected value of the net present value is greater than or equal to zero on the basis of knowing the probability of occurrence of various situations. A graphical method for intuitive use of probability analysis. Because this decision-making branch is drawn in a graph that resembles the branches of a tree, it is called a decision tree. In machine learning, a decision tree is a predictive model that represents a mapping relationship between object attributes (ie features) and object values ​​(target columns). Usually ID3 algorithm, C4.5 algorithm, and C5...

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Problems solved by technology

However, due to the inherent limitations of chi-square verification, CHAID cannot handle continuous variables well. It needs to discretize continuous variables. By default, CHAID automatically divides continuous variables into 10 segments for processing, but there may be omit

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