How do you adjust a random forest?
We will try adjusting the following set of hyperparameters:
- n_estimators = number of trees in the foreset.
- max_features = max number of features considered for splitting a node.
- max_depth = max number of levels in each decision tree.
- min_samples_split = min number of data points placed in a node before the node is split.
How to calculate feature importance in random forest?
The average over all trees in the forest is the measure of the feature importance. This method is available in scikit-learn implementation of the Random Forest (for both classifier and regressor). It is worth to mention, that in this method we should look at relative values of the computed importances.
Are there any drawbacks to the random forest method?
The drawbacks of the method is to tendency to prefer (select as important) numerical features and categorical features with high cardinality. What is more, in the case of correlated features it can select one of the feature and neglect the importance of the second one (which can lead to wrong conclusions).
How many decision trees are in a random forest?
Random forests consist of 4 –12 hundred decision trees, each of them built over a random extraction of the observations from the dataset and a random extraction of the features. Not every tree sees all the features or all the observations, and this guarantees that the trees are de-correlated and therefore less prone to over-fitting.
Which is the best parameter tuning for random forest?
AUC is a good way for evaluation for this type of problems. n_estimators represents the number of trees in the forest. Usually the higher the number of trees the better to learn the data. However, adding a lot of trees can slow down the training process considerably, therefore we do a parameter search to find the sweet spot.