Hands-On Gradient Boosting with Python: A Practical Introduction to XGBoost, LightGBM, and the Scikit-Learn Ecosystem
Disponibilidad:
En stock
En stock
Peso con empaque:
0.57 kg
0.57 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Sobre este producto
- Set up your Python machine learning environment with confidence
- Understand core concepts like decision trees, ensembles, and gradient boosting in plain English
- Build practical models with scikit-learn, XGBoost, and LightGBM for regression and classification
- Work on real-world projects such as house price prediction and credit risk scoring
- Tune hyperparameters, handle imbalanced data, and evaluate models with metrics like AUC, F1, and RMSE
- Use SHAP and LIME for model explainability so you can trust your predictions
- Save, load, and deploy your models so they are ready for real applications
- Clear explanations before any code
- Gradual progression from simple to advanced models
- Gentle reminders that confusion is part of learning
- Practical tips for debugging, improving, and reusing your work
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AR$55,373
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AR$107,971
49% OFF
AR$55,373
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