How do I tune parameters in regression models?

How do I tune parameters in regression models?

Tuning parameters in regression models involves adjusting settings like learning rate, regularization, and others to improve performance.

What are some common parameters I should consider?

Common parameters include the regularization strength (like L1 or L2), learning rate, and the number of iterations for optimization.

Can you explain how regularization affects the model?

Regularization helps prevent overfitting by adding a penalty for larger coefficients, which can improve model generalization.

What techniques can I use for parameter tuning?

You can use techniques like Grid Search, Random Search, or Bayesian Optimization to systematically explore parameter combinations.