Difference between Polynomial Regression and Neural Network
Feature/Aspect | Polynomial Regression | Neural Network |
---|---|---|
Structure | Single equation (polynomial) | Multi-layered (input, hidden, output layers) |
Flexibility | Limited to polynomial functions | High flexibility; handles complex non-linear relationships |
Complexity | Simple | Complex |
Interpretability | Easily interpretable | Less interpretable (black-box model) |
Training | Requires finding polynomial coefficients | Requires adjusting weights and biases through optimization |
Overfitting | Prone with higher-degree polynomials | Prone, especially with complex architectures |
Computational Needs | Less computational resources | More computational resources, especially for deep networks |
Data Requirements | Moderate | High; requires large labeled datasets |
Generalization | May not generalize well, especially with high-degree polynomials | Can generalize well with proper regularization and tuning |
Applicability | Suitable for simpler, non-linear data | Suitable for complex, high-dimensional data |
Versatility | Limited to regression problems | Versatile; applicable to various ML tasks |
Feature Engineering | May require manual feature selection for polynomial terms | Can automatically learn features from data |
Polynomial Regression vs Neural Network
In this article, we are going to compare polynomial regression and neural networks.