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you have decision tree classification and regression , where regression we use for continuous data
A decision tree can be used for both classification and regression tasks. In the case of a continuous dependent variable, such as in the diabetes dataset you mentioned, it would be used for regression. The algorithm works by recursively splitting the dataset into subsets based on the values of the independent variables, and at each split, the dependent variable is predicted using the mean or median value of the samples in the subsets. The goal is to create a tree of splits that results in the smallest possible prediction error. To apply this to the diabetes dataset, you would need to train the decision tree model using the independent variables in the dataset to predict the continuous dependent variable.
Running random forest algorithm with one variable