What is hyperparameter tuning and why is it important?

What is hyperparameter tuning and why is it important?

Hyperparameter tuning is the process of optimizing the parameters that govern the training of a machine learning model. It's crucial because it can significantly affect the model's performance.

Can you give me an example of hyperparameters?

Sure! Examples of hyperparameters include the learning rate, number of trees in a random forest, and the number of hidden layers in a neural network.

How do I perform hyperparameter tuning?

You can perform hyperparameter tuning using techniques like grid search, random search, or more advanced methods like Bayesian optimization.

What impact does it have on model accuracy?

Proper hyperparameter tuning can lead to improved model accuracy, better generalization to unseen data, and reduced overfitting.

Are there any tools that can help with this?

Yes, tools like Scikit-learn, Optuna, and Hyperopt can assist in automating the hyperparameter tuning process.