An in-depth look at decision tree models: Algorithm and problem discussion

Decision tree is a supervised machine learning model that is trained using labeled input and target data. It represents the decision-making process through a tree structure, and makes decisions based on the answers to the previous groups of tags/nodes. The advantage of a decision tree is that it imitates the logical flow of human thinking, making the results and processes easier to understand and explain. Unlike linear models, decision trees are able to handle nonlinear relationships between variables. It is mainly used to solve classification problems and classify or classify objects through models. Furthermore, in machine learning, decision trees can also be used to solve regression problems.

Structure of Decision Tree

Decision trees are built by recursive partitioning, with the root of the tree at the top. The root node contains all training data. Starting from the root node, each node can be split into left and right child nodes. Leaf nodes are end nodes without further divisions and are also called decision nodes.

Decision Tree Algorithm

CART Algorithm

CART (Classification and Regression Trees) is a decision tree algorithm used to handle classification and regression tasks. Decision trees work by splitting nodes into sub-nodes based on threshold values of attributes. CART uses the Gini index and variance reduction as indicators to determine the threshold for splitting. For classification and regression trees, CART uses the Gini coefficient to measure the purity of the data set and implements classification by splitting the decision tree. The CART algorithm is also suitable for multi-class features. For regression decision trees, the variance-reduced mean square error is used as the feature selection criterion, and the average value of each leaf node is utilized to minimize the L2 loss. Therefore, the CART algorithm can select the best split point based on the characteristics of the input data and build a decision tree model with good generalization ability.

ID3 algorithm

ID3 is a classification decision tree algorithm based on a greedy strategy, which builds a decision tree by selecting the best features that produce maximum information gain or minimum entropy. At each iteration, the ID3 algorithm divides features into two or more groups. Typically, the ID3 algorithm is suitable for classification problems without continuous variables.

Related reading: Decision tree algorithm principle

Decision tree over-fitting problem

Over-fitting means that the model over-emphasizes the characteristics of the training data, resulting in new data being encountered. or predictions of future results may be inaccurate. In order to better fit the training data, the model may generate too many nodes, making the decision tree too complex to interpret. While decision trees perform well at predicting training data, their predictions on new data can be inaccurate. Therefore, overfitting needs to be solved by adjusting model parameters, increasing the amount of training data, or using regularization techniques.

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