Contents
What is feature importance Sklearn?
Feature importance refers to a class of techniques for assigning scores to input features to a predictive model that indicates the relative importance of each feature when making a prediction.
Can feature importance be greater than 1?
You can’t have too many, it will only cost performance. Do not rely on feature importance too much. If one feature has a higher importance than another, it probably means that it is more important but you can’t be sure: For example, assume you copied one feature and added it as a new feature to the data.
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.