Anna Veronika Dorogush: Mastering gradient boosting with CatBoost | PyData London 2019

Описание: Gradient boosting is a powerful machine-learning technique that achieves state-of-the-art results in a variety of practical tasks. This tutorial will explain details of using gradient boosting in practice, we will solve a classification problem using the popular GBDT library CatBoost.

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0:00 - Introduction
1:49 - Intro to CatBoost
2:08 - Overview of the Presentation
2:39 - Intro to Gradient Boosting
6:08 - Numerical and Categorical Data with CatBoost
7:26 - Advantages of CatBoost
9:00 - Library Comparison (Quality)
9:45 - Speed
10:11 - Benchmarking (CPU & GPU)
11:55 - CPU vs GPU
12:50 - Prediction Time
13:24 - Tutorial
15:15 - Problem Statement
15:38 - CatBoost Library (Imports and related issues)
16:22 - Reading and Intro to the Data
18:17 - Exploring the data
19:36 - Training the Model with default parameters
22:16 - Creating the Pool Object
23:12 - Splitting the data (Train & Validation)
24:16 - Selecting the objective function
25:11 - STDOUT of training
28:32 - Plotting metrics while training
30:33 - Model Comparison (plotting after training)
32:39 - Finding the best model
35:05 - Cross-Validation
41:30 - Grid Search
44:40 - Overfitting Detector
49:18 - Overfitting Detector with eval metric
51:31 - Model Predictions
57:10 - Select Decision Boundary
1:01:04 - Model Evaluation (new dataset)
1:03:06 - Feature Importance
1:03:37 - Prediction Values Change
1:04:50 - Loss Function Change
1:07:49 - Shap Values
1:16:05 - Snapshotting
1:17:45 - Saving the Model
1:18:36 - Hyperparameters Tuning
1:23:07 - Speeding up Training and Reducing Model Size
1:23:35 - Additional Details about CatBoost Community
1:25:50 - Future Scope of CatBoost
1:26:22 - Questions and Suggestions

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