Artículo: AMZ-B0G674NTGL

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

Disponibilidad
Sin Stock
Peso con empaque
0.20 kg
Devolución
No
Condición
Nuevo
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

Producto prohibido

Este producto no está disponible

Este producto viaja de USA a tus manos en
Medios de pago Tarjetas de Débito y Crédito

Compra protegida

Disfruta de una experiencia de compra segura y confiable