Polynomial Regression vs Neural Network: Interpretability

What is Polynomial Regression?

Polynomial regression is a technique used to model the relationship between a dependent variable (what you’re trying to predict) and an independent variable (what you’re basing your prediction on) when that relationship isn’t straight line. Polynomial regressions are capable to fit curves by leveraging polynomial equations. Hence, the complexity of the curve is dependent on the degree of polynomial....

What are Neural Network?

Neural Networks are brain inspired machine learning models. The basic structure of neural network includes layers and activation functions....

Polynomial Regression vs Neural Network: Flexibility and Complexity

In terms of complexity, neural network offer more flexibility in comparison to polynomial regression. The neural networks are capable of capturing complex nonlinear relationships in the data and polynomial regression is much simpler hence, struggles to capture the patterns....

Polynomial Regression vs Neural Network: Performance

Neural Networks tend to outperform Polynomial Regression, especially when dealing with large datasets and complex relationships. They have the ability to learn from data hierarchically and adapt to diverse patterns....

Polynomial Regression vs Neural Network: Interpretability

Polynomial Regression provides straightforward interpretation due to its linear nature. It’s easier to understand the impact of each feature on the target variable. Neural Networks, however, are often considered black-box models, making it challenging to interpret the learned parameters....

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...

When to Use Polynomial Regression Instead of Neural Networks

There are certain scenarios where polynomial regression are a better option over neural network....