Performance Analysis of CHAID Algorithm for Accuracy

Decision tree algorithm belongs to the machine learning method of supervised learning, and it is one of the common techniques of data mining. CHAID algorithm is one of the common decision tree algorithms. The CHAID algorithm can be used for prediction as well as for classification. It is mainly used in market analysis, risk prediction and other fields. The second part tells the core idea of CHAID algorithm and the specific steps of the classification process, as well as the principle formula of chi-square detection and the specific steps of the calculation mainly used by CHAID algorithm. The third part describes the branch principle of CHAID algorithm and other decision tree algorithm, and analyses the accuracy of CHAID algorithm. Let us choose a relatively good decision tree algorithm according to the specific data situation, and apply the CHAID algorithm can also make some countermeasures according to some factors affecting the accuracy, which is more conducive to get a more accurate and better result.

I. Introduction

Since the 1990s, with the rapid development of information technology, the application of database systems has become more and more widespread, and at the same time database technology has entered a completely new stage, from just managing some simple data in the past to managing a wide variety of complex data such as images, videos, audio, graphics and electronic files generated by various devices, and the amount of data has become larger and larger.