What are the disadvantages and advantages of using a Decision Tree?
Following are the advantages of Decision Trees:
- They are able to identify and model complex patterns.
- Work well with both classification and regression problems
- Unaffected by outliers
- Easier to explain to non technical stakeholders. Complex Decision Trees can be explained just by creating their visual representations.
- Scaling and normalization are not needed
Following are the disadvantages of Decision Trees:
- Sensitive to overfitting
- Small change in data can cause instability in the model owing to use of recursive binary splitting
- They are computationally more intensive and take longer time to train than other classification algorithms.