1. Clear Visualization: This algorithm is simple to understand, interpret and visualize as the idea is mostly used in our daily lives. The output of a Decision Tree can be easily interpreted by humans.
2. Simple and easy to understand: Decision Tree works in the same manner as simple if-else statements which are very easy to understand.
3. This can be used for both classification and regression problems.
4. Decision Trees can handle both continuous and categorical variables.
5. No feature scaling required: There is no requirement of feature scaling techniques such as standardization and normalization in the case of Decision Tree as it uses a rule-based approach instead of calculation of distances.
6. Handles nonlinear parameters efficiently: Unlike curve-based algorithms, the performance of decision trees can’t be affected by the Non-linear parameters. So, if there is high non-linearity present between the independent variables, Decision Trees may outperform as compared to other curve-based algorithms.
7. Decision Tree can automatically handle missing values.
8. Decision Tree handles the outliers automatically, hence they are usually robust to outliers.
9. Less Training Period: The training period of decision trees is less as compared to ensemble techniques like Random Forest because it generates only one Tree unlike the forest of trees in the Random Forest.