The Classification of Mobile Phone Prices Based on Decision Tree

Classification is one of the common and basic problems in the field of machine learning. There are many methods to deal with this problem, such as logistic regression, K-Nearest Neighbors, Convolutional Neural Networks, etc. However, for those who have just started machine learning, decision trees are a very suitable method. The reason for this is that decision trees are very readable. Nonetheless, when actually applying decision trees to solve classification tasks, many problems are often encountered, such as how to clean the data set and how to tune hyperparameters. In this article, I will take the specific task of classifying mobile phone prices as an example to discuss in detail the construction process of the decision tree model. The purpose of the discussion is to help researchers who want to learn decision trees understand the structure of this model, understand the problems that may be encountered in the process of building the model, and recognize the strengths as well as the weaknesses about this model such as the readability of decision trees and its sensitivity to data quality.