What are the limitations of linear models in machine learning?

What are the limitations of linear models in machine learning?

Linear models assume a linear relationship between input and output, which may not always hold true.

Can you give me examples of situations where linear models fail?

Sure! Linear models struggle with non-linear relationships, high-dimensional data, and interactions between features.

How do these limitations affect model performance?

These limitations can lead to underfitting, where the model fails to capture the underlying patterns in the data.

Are there alternatives to linear models that can handle these issues?

Yes, alternatives like decision trees, random forests, and neural networks can model complex relationships better.