Is feature scaling necessary for XGBoost?
Your rationale is indeed correct: decision trees do not require normalization of their inputs; and since XGBoost is essentially an ensemble algorithm comprised of decision trees, it does not require normalization for the inputs either.
Why do decision trees not need scaling?
Decision trees and ensemble methods do not require feature scaling to be performed as they are not sensitive to the the variance in the data.
How is feature importance calculated in XGBoost model?
A trained XGBoost model automatically calculates feature importance on your predictive modeling problem. These importance scores are available in the feature_importances_ member variable of the trained model.
Why is XGBoost not sensitive to monotonic transformations?
XGBoost is not sensitive to monotonic transformations of its features for the same reason that decision trees and random forests are not: the model only needs to pick “cut points” on features to split a node. Splits are not sensitive to monotonic transformations: defining a split on one scale has a corresponding split on the transformed scale.
How to find the permutation importance of XGBoost?
The permutation importance for Xgboost model can be easily computed: The visualization of the importance: The permutation based importance is computationally expensive (for each feature there are several repeast of shuffling). The permutation based method can have problem with highly-correlated features. Let’s check the correlation in our dataset:
Why does root split at feature 2 in XGBoost?
In following diagram, root splits at feature 2. because all its descendants should be able to interact with it, so at the second layer all 4 features are legitimate split candidates for further splitting, disregarding specified constraint sets. This has lead to some interesting implications of feature interaction constraints.