Ensemble Learning Visualizer
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Ensemble Learning Visualizer
This interactive tool helps visualize how different ensemble learning methods work. Select a method below to explore step-by-step:
- Bagging: Trains multiple models on random subsets of data and combines them through equal voting/averaging
- Boosting: Trains models sequentially, with each model focusing on errors made by previous models
- Stacking: Trains multiple base models and a meta-model that learns how to best combine their predictions
Bagging
Boosting
Stacking
Bagging (Bootstrap Aggregating)
Bagging creates multiple training subsets by random sampling with replacement, trains a model on each subset, and combines their predictions.
Class 1
Class 2
Decision Boundary
Bagging Sample
5
80%
Step: 0/0
Click “Next Step” to start the visualization.
Boosting
Boosting trains models sequentially, with each model giving more attention to instances that previous models struggled with.
Class 1
Class 2
Decision Boundary
Final Ensemble
Higher Weight
5
Step: 0/0
Click “Next Step” to start the visualization.
Stacking
Stacking trains multiple base models and a meta-model that learns how to optimally combine their predictions.
Class 1
Class 2
Decision Boundary
Meta-model
3
Step: 0/0
Click “Next Step” to start the visualization.
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