Linear or simple non-linear boundaries (polynomial, radial)
Requires manual feature engineering and domain expertise
Highly interpretable - clear feature importance and decision rules
Complex, highly non-linear boundaries that can capture intricate patterns
Automatically learns hierarchical features from raw data
Less interpretable - difficult to understand internal representations
Neural networks excel at learning complex, non-linear relationships in data, while traditional ML algorithms are better suited for simpler patterns and when interpretability is crucial. The choice depends on your data complexity, interpretability requirements, and computational resources.
| Aspect | Traditional ML | Neural Networks |
|---|---|---|
| Data Requirements | Small to medium datasets | Large datasets needed |
| Training Speed | Fast training | Slow training |
| Computational Resources | Minimal resources | High computational cost |
| Feature Engineering | Manual feature engineering required | Automatic feature learning |
| Interpretability | Highly interpretable | Black box |
| Complex Pattern Learning | Limited to simple patterns | Excellent at complex patterns |
| Overfitting Risk | Moderate | High (needs regularization) |
| Hyperparameter Tuning | Few parameters | Many parameters |