Decision Tree: Advantages and Disadvantages

Advantage

• It is very easy, effective and simple.
• It can handle both categorical and numeric data very efficiently as compared to other algorithms.
• Missing values present in the data set does not affect the decision tree.
• Preprocessing of data is not required.
• Results that the decision tree generate does not require any prior knowledge of statistical or mathematics.

Disadvantages

• If data is not discretized properly, then it will give an inaccurate result.
• It is sometimes unstable and cannot be reliable because of alteration in data which causes a bad structure of the decision tree which affects the model.
• It works very badly in the case of regression as it does not support data with too much variation.