Ensemble Methods combine predictions from multiple models to create a stronger, more robust predictor that often outperforms any individual model in the ensemble.
Core Principle:
• Diversity: Different models make different errors
• Aggregation: Combine predictions intelligently
• Robustness: Reduce variance and improve generalization
• Wisdom of Crowds: Multiple perspectives yield better decisions
🧠 The Wisdom of Crowds
"The many are smarter than the few" - Just as a group of people can collectively make better decisions than individuals, multiple models can achieve superior performance!
Ensemble Strategies
🎒
Bagging
Train models on different subsets of training data
🚀
Boosting
Sequential training focusing on difficult examples
🏗️
Stacking
Meta-learner combines base model predictions
📸
Snapshot Ensembles
Multiple models from single training run
Ensemble Prediction Process
Individual Models
Model 1
Acc: 85%
Model 2
Acc: 87%
Model 3
Acc: 83%
→
Ensemble Result
Combined Model
Acc: 91%
Better than any individual!
Result: Ensemble typically outperforms individual models through error reduction