Common algorithms for decision

hmdahsan113

trees ID3 algorithm: Select split attributes based on information gain. C4.5 algorithm: An improved version of the ID3 algorithm, which uses information gain rate to select split attributes, can overcome the problem that the ID3 algorithm tends to select attributes with more values. CART algorithm: Supports classification and regression, and uses Gini index or variance to select split attributes. Application scenarios of decision trees Classification problems: For example, customer churn prediction, disease diagnosis, etc.


Regression problems: For example, house Email List price prediction, sales prediction, etc. Anomaly detection: Discover abnormal samples in the data. Decision tree pruning In order to prevent overfitting, decision trees usually need to be pruned. Pruning can be divided into two methods: pre-pruning and post-pruning: Pre-pruning: Set some stopping conditions during the tree growth process to stop the tree growth in advance. Post-pruning: First generate a complete tree, then start pruning from the leaf node upwards and remove some subtrees.





Summary of the advantages and disadvantages of decision trees Advantages Disadvantages Strong interpretability Easy to overfit Handle mixed type data Instability No feature scaling required Biased towards attributes with more values Export to Sheets Summary Decision trees are a simple and powerful classification and regression algorithm that has been widely used in data mining. By choosing the right algorithm and pruning techniques, the performance of decision trees can be effectively improved.