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Hands-On Gradient Boosting with Python: A Practical Introduction to XGBoost, LightGBM, and the Scikit-Learn Ecosystem

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Peso con empaque:
0.57 kg

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Sí

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Producto de:
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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Hands-On Gradient Boosting with Python: A Practical Introduction to XGBoost, LightGBM, and the Scikit-Learn Ecosystem

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