Advantages and disadvantages of decision trees?

Advantages:

• Compared to other algorithms decision trees requires less effort for data preparation during pre-processing.
• A decision tree does not require normalization of data.
• A decision tree does not require scaling of data as well.
• Missing values in the data also do NOT affect the process of building a decision tree to any considerable extent.
• A Decision tree model is very intuitive and easy to explain to technical teams as well as stakeholders.



Disadvantage:

• A small change in the data can cause a large change in the structure of the decision tree causing instability.
• For a Decision tree sometimes calculation can go far more complex compared to other algorithms.
• Decision tree often involves higher time to train the model.
• Decision tree training is relatively expensive as the complexity and time has taken are more.
• The Decision Tree algorithm is inadequate for applying regression and predicting continuous values.