Gradient Boosting: Implementation Using Scikit-learn

In this lesson, we’ll look into the testing phase of gradient boosting, building upon the trained model that we previously developed. Our main objective is to utilize this trained model to make predictions on a test dataset. To validate the performance of our implementation, we will compare our results with those obtained from GradientBoostingRegressor provided by the scikit-learn library.

Training of gradient boosting regressor

Before proceeding to the testing phase, we’ll consolidate all the code widgets of the previous lesson to review and understand the progress we’ve made so far. Then, we’ll evaluate the effectiveness of our trained model on unseen data.