Avoid Overfitting by Early Stopping with XGBoost

Overfitting is a problem with sophisticated non-linear learning algorithms like gradient boosting.

Here you will discover how you can use early stopping to limit overfitting with XGBoost in Python.

By the end you will know:

• About early stopping as an approach to reducing overfitting of training data?
• How to monitor the performance of an XGBoost model during training and plot the learning curve?
• How to use early stopping to prematurely stop the training of an XGBoost model at an optimal epoch?

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Next: Tune Multithreading Support for XGBoost