kardesyazilim/comparison_table.md

## comparison_table.md

      
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              comparison_table.md
            
          
Method
Training Style
Error Focus
Variance
Bias
Typical Use Case


Bagging
Parallel (independent)
Reduces variance
↓↓
↔
High-variance models (e.g., deep trees)


Boosting
Sequential
Reduces bias
↓
↓↓
Weak learners; structured/tabular data


Stacking
Hybrid
Leverages diversity
↓
↓
When you have diverse strong models


↓ = reduction, ↔ = little change
Method	Training Style	Error Focus	Variance	Bias	Typical Use Case
Bagging	Parallel (independent)	Reduces variance	↓↓	↔	High-variance models (e.g., deep trees)
Boosting	Sequential	Reduces bias	↓	↓↓	Weak learners; structured/tabular data
Stacking	Hybrid	Leverages diversity	↓	↓	When you have diverse strong models
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