Gradient boosting framework, greatly

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improving efficiency and accuracy. 4 , the construction process of decision trees and random forests. Detailed explanation of the construction steps of decision trees. Data preparation first preprocesses the data, including missing value filling, outlier processing, feature encoding and other operations. Feature selection calculates the information of all features on each internal node. Gain I or Gini impurity R selects the feature with maximum gain and minimum impurity as the dividing criterion. Generate branch divides the data set into subsets based on the best split point of the selected feature and creates a branch for the node. Recursive growth for each sub- The set repeats the above process until the stopping c

onditions are met, such as reaching the preset maximum depth, the number of samples contained in leaf nodes is less than the threshold, or the information gain is no longer significantly improved, etc. In order to prevent overfitting, pruning Rich People Phone Number List optimization can be done through post-pruning or pre-pruning Method to simplify the decision tree structure and improve the model generalization ability. The construction process of random forest r sampling extracts multiple sample subsets from the original training set with replacement to form multiple data sets for training different decision trees. Feature randomization is for Each decision tree only considers a random subset at each split, usually a fixed proportion of all features, to select and split the optimal features. Decision tree generation trains a decision tree independently on each sampled data set and There is no need for pruning because a single tree is allowed to grow freely, which helps to increase the diversity of the integrated model. In the prediction stage, for new input instances,




the prediction classification task is performed separately through all decision trees, and the majority voting regression task is used to take the average as the final result. Feature importance evaluation uses the frequency of each feature being selected in all decision trees constructed or the degree of impurity reduction to measure the importance of features. 5. Practical strategies and parameter adjustment suggestions for decision trees and random forests in practical applications Parameter adjustment is crucial. For example, for decision trees, it is necessary to set the maximum depth of the appropriate tree, the minimum number of node samples,