What is model parameter tuning in machine learning?

What is model parameter tuning in machine learning?

Model parameter tuning is the process of optimizing the parameters of a machine learning model to improve its performance.

Why is it important to tune parameters?

Tuning parameters is crucial because it helps the model generalize better to unseen data, reducing overfitting and improving accuracy.

What are some common techniques for parameter tuning?

Common techniques include grid search, random search, and Bayesian optimization, each with its own advantages.

How do I know which parameters to tune?

Start by understanding the model's documentation, then focus on parameters that significantly impact performance, like learning rate or regularization.