Evaluate Gradient Boosting Models with XGBoost

The goal of developing a predictive model is to develop a model that is accurate on unseen data.

This can be achieved using statistical techniques where the training dataset is carefully used to estimate the performance of the model on new and unseen data.

Here you will discover how you can evaluate the performance of your gradient boosting models with XGBoost in Python.

By the end you will know.

• How to evaluate the performance of your XGBoost models using train and test datasets?
• How to evaluate the performance of your XGBoost models using k-fold cross validation?

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