Machine Learning

1) Easy to Understand and Interpret: Decision trees are simple to visualize and explain to stakeholders who may not have technical backgrounds.
2) Handles Both Numeric and Categorical Data: They can process different types of input variables, making them versatile for various tasks.
3) Requires Little Data Preparation: Unlike other models, decision trees do not require scaling or normalization of data.
4) Captures Nonlinear Relationships: Decision trees can model complex patterns and interactions that linear models may miss.
Additionally, they can be combined in ensembles like Random Forests for improved accuracy and stability.