How do different regression models compare in terms of accuracy?

How do different regression models compare in terms of accuracy?

Different regression models, like linear regression, ridge regression, and lasso regression, have varying accuracy depending on the dataset and the underlying relationships. Linear regression is simple and interpretable, but may not capture complex patterns. Ridge and lasso regression add regularization, which can improve accuracy by preventing overfitting.

What factors should I consider when choosing a regression model?

When choosing a regression model, consider factors like the nature of your data, the relationship between variables, the presence of multicollinearity, and whether you need interpretability or predictive power. Additionally, evaluate model performance using metrics like R-squared, RMSE, and cross-validation.

Can you explain the difference between ridge and lasso regression?

Certainly! Ridge regression adds a penalty equal to the square of the magnitude of coefficients, which helps in reducing model complexity. Lasso regression, on the other hand, adds a penalty equal to the absolute value of the coefficients, which can lead to some coefficients being exactly zero, effectively performing variable selection.

Which model would you recommend for a small dataset?

For a small dataset, I would recommend starting with linear regression due to its simplicity and interpretability. However, if you suspect overfitting, you might consider ridge regression, as it can help stabilize the estimates by adding regularization.