What are the differences between linear regression and other models?

What are the differences between linear regression and other models?

Linear regression is a simple model that assumes a linear relationship between input and output. Other models, like decision trees or neural networks, can capture more complex patterns.

Can you explain when to use linear regression instead of those other models?

Use linear regression when the relationship is expected to be linear and the data is not too complex. It's also great for interpretability.

What are some limitations of linear regression?

Linear regression can struggle with non-linear relationships, outliers, and multicollinearity among predictors.

Are there scenarios where linear regression performs better than more complex models?

Yes, in cases with a clear linear trend and fewer features, linear regression can outperform complex models due to its simplicity and lower risk of overfitting.