Ensemble learning is a machine learning technique where multiple individual models (called base learners) are combined to form a stronger predictive system. By aggregating diverse models, ensembles improve accuracy, stability, and generalization compared to using a single model.

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How Ensemble Learning Works

An ensemble generates predictions from multiple models and merges the outputs using techniques such as:
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• Averaging (for regression tasks)
  
  
• Majority voting (for classification tasks)
  
  
• Weighted blending or stacking
  
  

The goal is to reduce variance, minimize overfitting, and improve predictive performance.

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Common Ensemble Methods

Bagging (Bootstrap Aggregating)

Trains multiple instances of the same model using random data subsets, then aggregates predictions.
Example: Random Forest

Boosting

Trains models sequentially, with each new model correcting errors from the previous one.
Examples: AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost

Stacking (Stacked Generalization)

Uses different types of models and a meta-model that learns how to combine their predictions.

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Why It Matters

Ensemble learning is widely used in:
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• Finance and fraud detection
  
  
• Healthcare diagnostics
  
  
• Cybersecurity anomaly detection
  
  
• NLP and recommendation systems
  
  

It remains one of the most reliable strategies for improving model performance, especially in real-world and production environments.

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Related Terms

• Model Evaluation
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• Gradient Boosting
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• Cross-Validation