rickiepark/handson-gb
XGBoost와 사이킷런으로 배우는 그레이디언트 부스팅
This project provides practical code examples for building powerful predictive models using gradient boosting. It takes raw data, processes it through advanced machine learning techniques, and outputs highly accurate regression and classification models. Data scientists, machine learning engineers, and analysts looking to improve prediction accuracy would use this.
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Use this if you need to build fast, efficient, and highly accurate predictive models for tasks like forecasting, fraud detection, or customer churn analysis.
Not ideal if you are looking for a simple, out-of-the-box solution without diving into the technical details of model building and hyperparameter tuning.
Stars
27
Forks
21
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 10, 2025
Commits (30d)
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