How to Configure the Gradient Boosting Algorithm - Machine Learning Mastery

We can see a few interesting things in this table. In a similar talk by Owen at ODSC Boston 2015 titled "Open Source Tools and Data Science Competitions", he again summarized common parameters he uses: We can see some minor differences that may be relevant. Finally, Abhishek Thakur, in his post titled "Approaching (Almost) Any Machine Learning Problem" provided a similar table listing out key XGBoost parameters and suggestions for tuning. The spreads do cover the general defaults suggested above and more. It is interesting to note that Abhishek does provides some suggestions for tuning the alpha and beta model penalization terms as well as row sampling. You can develop and evaluate XGBoost models in just a few lines of Python code.