1) Efficient even with high dimensional (features) data
2) Considers the importance of each feature
3) Judges correlations between features
4) Limits overfitting
5) Simplifies the parallel method
6) Can balance errors in unbalanced datasets
7) Can account for loss of features in the dataset

University of Michigan - Ann Arbor

Decision Trees are **non-parametric**, since we are not making any assumptions about the underlying function $$f$$. Consequently, decision trees have very low structural error (bias). By averaging the output of many trees in a random forest, we are lowering the variance of the model, while keeping the bias the same. 


Why Random Forests?

A really good paper, that explains random forests very well : 
[www.stat.berkeley.edu/~breiman/randomforest2001.pdf](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) 

Random Forests

1) In the case of noisy data, can fit poorly
2) Potential for inaccurate attribute weights when uneven frequency of attribute distribution
3) Requires more data to store the additional trees


Learn Before

Related