Combine up to 10 models · averaging · voting · stacking concept · Fleiss’ κ
🤖 Models
Max 10 models. Accuracy 0–1 (e.g., 0.85).
⚙️ Method & weights
Weights will be normalized automatically.
📊 Sample predictions
Enter class labels (e.g., A/B) or probabilities (0–1). For voting use discrete labels.
📈 Results
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Ensemble prediction
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Expected accuracy gain
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Agreement rate
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Fleiss' κ
🤝 Model agreement per sample
🧩 Diversity & returns
When ensembles help most: diverse models (low agreement) with moderate accuracy. Below: simulated diminishing returns as more models are added (based on avg diversity).
📋 Method comparison
● Simple avg — equal weight, good for probabilities.