regression decision tree

The decision tree regressor is a regression model based on the decision tree algorithm, used to predict the value of continuous variables. It divides the input feature space into several subspaces by building a decision tree, and each subspace corresponds to a predicted value. During prediction, according to the value of the input feature, the corresponding leaf node is recursively searched from top to bottom along the decision tree to obtain the corresponding predicted value. The decision tree regressor has the advantages of being simple and easy to interpret, can handle multi-dimensional features, and adapt to nonlinear relationships. It is often used in fields such as housing price prediction, stock price prediction, and product sales prediction.

The decision tree regressor algorithm predicts continuous variables based on feature space partitioning. The specific steps are as follows:

1. According to the features and sums in the data set Target variable, select an optimal feature as the root node, and divide the sample set into different subsets.

For each subset, repeat step 1, select the best features as child nodes, and continue dividing the subset into smaller subsets until only one sample is left or no more samples are left. point.

3. For each leaf node, calculate the average of the samples as the predicted value.

4. During prediction, according to the value of the input feature, the corresponding leaf node is recursively searched from top to bottom along the decision tree to obtain the corresponding predicted value.

5. When selecting optimal features, indicators such as information gain, information gain ratio or Gini index are usually used to measure the importance of features. When splitting samples, greedy algorithms, pruning algorithms, etc. can be used to reduce the complexity and generalization error of the model.

It should be noted that decision tree regressors are prone to overfitting problems, so pruning and other operations are often required to improve prediction performance.

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