How to calculate feature importance with random forest?

How to calculate feature importance with random forest?

Calculating the feature or variable importance with a Random Forest model, tells us which of the features of our data are the most helpful towards our goal, which can be both Classification and Regression.

Why are random forests important in machine learning?

Random forest feature importance. Random forests are among the most popular machine learning methods thanks to their relatively good accuracy, robustness and ease of use. They also provide two straightforward methods for feature selection: mean decrease impurity and mean decrease accuracy.

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.

What is mean decrease impurity in a random forest?

Mean decrease impurity Random forest consists of a number of decision trees. Every node in the decision trees is a condition on a single feature, designed to split the dataset into two so that similar response values end up in the same set. The measure based on which the (locally) optimal condition is chosen is called impurity.

Why are feature correlations biased in random forests?

In my last post, I investigated claims by Altmann, et al. that feature importance scores from Random Forests (RFs) were biased for categorical variables. I demonstrated that the bias was due to the encoding scheme. Altmann, et al. also claimed that RFs are biased in the presence of correlation between variables.

Why are binary features important in a random forest?

This is because these kinds of variables, because of their nature have a higher chance of appearing more than once in an individual tree, which contributes to an increase in their importance. If a binary feature is really relevant though, it will still be reflected in the feature importance ranking [1].

Why are PCA features not correlated with each other?

As a result, the PCA features won’t be correlated with each other. Unfortunately, interpreting the feature importance from PCs is not necessarily easy, limiting the value of PCA as a solution if having a descriptive model is important. If feature selection is the primary goal, we can use an alternative approach.

How to get insight into a random forest?

One way of getting an insight into a random forest is to compute feature importances, either by permuting the values of each feature one by one and checking how it changes the model performance or computing the amount of “impurity” (typically variance in case of regression trees and gini coefficient or entropy in case…

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).