The sklearn.tree.DecisionTreeRegressor is a class in the Python scikit-learn library that implements the decision tree regression algorithm. It uses a binary decision tree to make predictions on continuous target variables. The decision tree is built by recursively splitting the data based on the feature that provides the most information gain or reduction in impurity. This algorithm is commonly used for solving regression problems, where the goal is to predict a continuous output variable based on the values of input features. It can handle both categorical and numerical features, and can be trained on datasets of any size. The DecisionTreeRegressor class provides various parameters to control the complexity of the tree, such as the maximum depth, maximum number of leaf nodes, and minimum number of samples required to perform a split. In addition, it supports features such as feature scaling, missing value imputation, and handling of categorical variables using one-hot encoding. Once trained, the decision tree can be used to predict the output variable for new unseen instances. Overall, sklearn.tree.DecisionTreeRegressor offers a powerful and flexible tool for regression tasks in machine learning.
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