Day #43 - Random Forest - One Page Summary

Consider how the mighty random forest...[From linkedin Post]
1. Handles both classification and regression.
2. Works with both categorical and numeric data.
3. Doesn't require centering/scaling of numeric data.
4. Is robust to outliers and over-fitting.
5. Works well on many business problems with hyperparameter default values.
6. Estimates generalization error.
7. Provides insights into feature importance.
8. Can be trained in parallel.
9. Provides an intuitive vehicle for understanding and working the bias-variance trade-off.
10. Supports problems with complex decision boundaries elegantly. 

Happy Learning!!!