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

Number of Models: 5

Sample Size (%): 80%

Step: 0/0

Click “Next Step” to start the visualization.

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

Number of Models: 5

Step: 0/0

Click “Next Step” to start the visualization.

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

Number of Base Models: 3

Step: 0/0

Click “Next Step” to start the visualization.

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