Contents
How does Decision Tree calculate feature importance?
Feature importance is calculated as the decrease in node impurity weighted by the probability of reaching that node. The node probability can be calculated by the number of samples that reach the node, divided by the total number of samples. The higher the value the more important the feature.
How do you get feature important?
Method #1 — Obtain importances from coefficients Probably the easiest way to examine feature importances is by examining the model’s coefficients. For example, both linear and logistic regression boils down to an equation in which coefficients (importances) are assigned to each input value.
How to compute feature importance for scikit-learn random forest?
The 3 ways to compute the feature importance for the scikit-learn Random Forest were presented: built-in feature importance permutation based importance In my opinion, it is always good to check all methods, and compare the results. I’m using permutation and SHAP based methods in MLJAR’s AutoML open-source package mljar-supervised.
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).
Which is the most important feature in the random forest?
The impurity-based feature importance ranks the numerical features to be the most important features. As a result, the non-predictive random_num variable is ranked the most important! This problem stems from two limitations of impurity-based feature importances: impurity-based importances are biased towards high cardinality features;
How are features shuffled in a forest of trees?
Features are shuffled n times and the model refitted to estimate the importance of it. Please see Permutation feature importance for more details. We can now plot the importance ranking. The same features are detected as most important using both methods. Although the relative importances vary.