02:39 Intro to Gradient Boosting 07:25 Catboost Advantages 14:47 Tutorial Starts 19:37 Training The First Model 22:16 Working with Pool - Catboost's Data Container 24:16 Objective Function & Standard Output 28:41 Metrics & Plotting 30:37 Model Comparison & Best Iteration 35:03 Cross-Validation 41:30 Using Catboost with Sklearn's Grid Search 44:42 Overfitting Detector 51:29 Making Prediction with Catboost 56:49 Select Decision Boundary 01:01:03 Metric Evaluation On New Data Sets 01:03:05 Feature Importance 01:16:06 Snapshotting & Saving Model 01:18:36 Hyperparameter Tunning 01:23:35 Outtro 01:26:22 Q&A
