If your XGBoost model is overfitting, what should you do first?

Question

BlackTom AI · Accepted Answer

The question asks about the first step to take when an XGBoost model is overfitting, so we need to evaluate what each option would do to model complexity and generalization.
Option 1: Increase n_estimators. In XGBoost, adding more trees (estimators) can improve performance on training data and potentially on validation data, but it often increases model capacity and can worsen overfitting if not counterbalanced by other regularization or subsampling. Therefore, as a first move to combat overfitting, simply increasing n_estimators is usually not advisable and may exacerbate the problem unless paired with other controls. 
Option 2: Reduce max_depth. Lowering the maximum depth reduces the complexity of each tree, limiting the model’s ability to fit noise in the data. This is a common and effective first step to reduce overfitting, as shallower trees tend to generalize better by capturing only broader patterns rather than intricate, dataset-specific quirks. 
Option 3: Reduce min_samples_split. This parameter (often associated with splitting criteria in some libraries) corresponds to how many samples are required to consider a split. In XGBoost, the analogous or relevant regularization controls are different (e.g., min_child_weight, gamma, subsample, colsample_bytree). Since min_samples_split is not a standard XGBoost parameter, changing it would either have no effect or be ineffective in addressing overfitting within XGBoost’s actual parameter set. Consequently, this option is not a reliable first move for controlling overfitting in XGBoost.
In summary, modifying the depth of trees directly targets model capacity and generalization, making it a more principled first step to address overfitting, while the other options either risk increasing overfitting or reference parameters that don’t apply in XGBoost.

Introduction to Machine Learning & AI - DAT-5329 - BMBAN2 In-Class Knowledge Check #2 (Remotely Proctored)

If your XGBoost model is overfitting, what should you do first?

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