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you have decision tree classification and regression, where regression we use for continuous data
Decision tree is a classification algorithm that can be used to predict a categorical outcome. However, it can also be used for regression tasks by treating the dependent variable as continuous. One way to apply decision tree to a diabetes dataset with a continuous dependent variable is to convert the continuous variable into a categorical variable by creating bins and then using the decision tree algorithm to predict the binned outcome. Another way would be to use a decision tree algorithm specifically designed for regression tasks such as the CART (Classification and Regression Tree) algorithm.