What is the role of random forests when using decision trees?
A random forest is simply a collection of decision trees whose results are aggregated into one final result. Their ability to limit overfitting without substantially increasing error due to bias is why they are such powerful models. One way Random Forests reduce variance is by training on different samples of the data.
How can decision trees overcome problems?
Issues in Decision Tree Learning
- Extract a validation set not used for training from the training set and use this to check for overfitting.
- Then use statistical tests, eg.
- A variation of the above is to use MDL to check if modifying the tree increases its MDL with respect to the validation set.
How are decision trees and random forests used?
If used for a classification problem, the result is based on majority vote of the results received from each decision tree. For regression, the prediction of a leaf node is the mean value of the target values in that leaf. Random forest regression takes mean value of the results from decision trees.
How is the importance of a decision tree calculated?
The importance for each feature on a decision tree is then calculated as: These can then be normalized to a value between 0 and 1 by dividing by the sum of all feature importance values: The final feature importance, at the Random Forest level, is it’s average over all the trees.
How to control the number of features in a random forest?
The number of features used for each tree in a random forest can be controlled with max_features parameter. Bootstrap samples and feature randomness provide the random forest model with uncorrelated trees.
Why are decision trees prone to overfitting?
However, choosing the best result at a given step does not ensure you will be headed down the route that will lead to the optimal decision when you make it to the final node of the tree, called the leaf node. 2. Decision trees are prone to overfitting, especially when a tree is particularly deep.