Decision Tree Regressor

Decision Trees Regressor predicts the value of a target variable by learning simple decision rules inferred from the data features.

Advantages:

• It requires no pre-processing of data like the normalization and the scaling of data Missing values in the data also do not affect

Disadvantages:

• A small change in the data can cause a large change
• It is computational expensive