Explore the capabilities of AutoXGB, a Python library for Automated Machine Learning, in this 24-minute tutorial. Learn about the library developed by Kaggle Grandmaster Abhishek Thakur, which simplifies the process of creating XGBoost models, tuning with Optuna, and serving with FastAPI. Dive into a practical demonstration using a Tabular Playground Kaggle Competition dataset. Begin with an introduction to AutoXGB, followed by an explanation of its parameters. Then, apply AutoXGB to build a model on the Kaggle competition dataset. Access additional resources, including the AutoXGB GitHub repository and Kaggle notebook, to further enhance your understanding of this powerful AutoML tool.
Overview
Syllabus
AutoXGB - Automated ML with xgboost + optuna + fastapi | Kaggle Demo
Taught by
1littlecoder
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