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1. Introduction to Ensemble Learning
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2. Models in Scikit-learn
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3. Beyond Scikit-learn: Popular External Libraries
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4. Practical Tips
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5. Summary Table
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6 Ensemble Model
This page provides a comprehensive overview of ensemble learning in scikit-learn and related libraries. Here's a summary of the key sections:
- Introduction to Ensemble Learning: Explains how ensemble models combine multiple simpler models to make better predictions.
- Scikit-learn Implementation: Covers various ensemble methods including:
- Bagging models (RandomForest, ExtraTrees)
- Boosting models (AdaBoost, GradientBoosting)
- Stacking models
- Voting models
- Isolation models for anomaly detection
- External Libraries: Details popular frameworks outside scikit-learn:
- XGBoost
- LightGBM
- CatBoost
- H2O.ai
- Practical Tips: Includes guidance on:
- Hyperparameter tuning
- Model selection
- Cross-validation
- Computational efficiency considerations
The page concludes with a comprehensive summary table listing all ensemble models, their import statements, and types (classification/regression).