Model Complexity and Its Impact
Overly simple models underfit, and overly complex models overfit.
The relationship between model complexity and performance is a fundamental concept in machine learning that influences both bias and variance.
For better generalization performance, we prefer biased models within the Bias-Variance Trade-off to prevent the high variance that occurs in unbiased models
- Simple Models (Low Complexity)
- Have large bias but small variance
- Tend to underfit the data
- Use fewer parameters
- Complex Models (High Complexity)
- Have small bias but large variance
- Tend to overfit the data
- Use many parameters
This trade-off suggests there might be fundamental limits to artificial intelligence capabilities, similar to the uncertainty principle in physics - we may need to balance between model complexity and generalization ability.
Bias-Variance Decomposition (Risk)
