Decision Trees

The basic principle of decision trees is similar to the processing mechanism of human beings when making choices, and it has its application scenarios in both machine learning and deep learning. Among them, the widely used random forest is an ensemble learning algorithm constructed based on decision trees. This chapter will first take the binary classification task as an example to explain the basic principle of decision trees, then introduce several core concepts related to the partition selection of decision trees, and finally provide code examples to help readers better understand this algorithm. Consider a binary classification task where we seek to develop a model from existing data to categorize new instances. This classification process inherently constitutes a decision-making mechanism. Binary classification is a prevalent problem type encountered in practical scenarios. For example, during triage at hospital receptions, nurses classify patients into emergency and non-emergency cases based on symptom severity. Initial assessments typically involve measuring vital signs, such as body temperature, and detecting fever.