Common XGBoost Mistakes to Avoid
Indrajit S.
![Indrajit S.]()
Indrajit S.
Senior Data Scientist @ Citi | GenAI | Kaggle Competition Expert | PHD research scholar in Data Science
Using Default Hyperparameters
- Why Wrong: Different datasets need different settings
- Fix: Always tune learning_rate, max_depth, min_child_weight based on your data size and complexity
Not Handling Class Imbalance
- Why Wrong: Leads to biased models favoring majority class
- Fix: Use scale_pos_weight or class_weight parameters
Ignoring Feature Importance
- Why Wrong: Redundant/noisy features hurt performance
- Fix: Use feature_importances_ to remove low-impact features
Overfitting with Deep Trees
- Why Wrong: Deep trees memorize training data
- Fix: Limit max_depth (3-10), use early stopping
Wrong Evaluation Metric
- Why Wrong: Default metrics may not match business goals
- Fix: Choose appropriate eval_metric (auc, error, rmse)
Not Scaling Features
- Why Wrong: While XGBoost is scale-invariant, extreme values cause instability
- Fix: Use StandardScaler or RobustScaler
Insufficient Cross-Validation
- Why Wrong: Single train-test split may give unreliable results
- Fix: Use k-fold CV with appropriate stratification
Memory Issues with Large Datasets
- Why Wrong: Default 'exact' method is memory-intensive
- Fix: Use 'hist' method, adjust max_bin parameter
#DataScience #XGBoost #MachineLearning