[Sklearn] Data classification prediction based on random forest algorithm (Excel can directly replace data)
1. Model Principle
Random Forest is an ensemble learning method that builds powerful classification or regression models by combining multiple decision trees. The model principle and mathematical model of random forest are as follows:
1.1 Model principle
Random forest is an ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of predictions. Each decision tree is trained independently, and their predictions are combined to form the final prediction. The main idea of Random Forest is to build a "forest" where each tree is a classifier and each classifier is trained on a random subset of the data. At prediction time, the results of all classifiers are combined by voting or averaging.
The main steps of random forest:
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Random sampling (Bootstrap sampling): Randomly select multiple samples from the original training data, allowing the same sample to appear multiple times in one sampling.