What are the best practices for hyperparameter tuning in AI training?

What are the best practices for hyperparameter tuning in AI training?

Some best practices include using grid search or random search, cross-validation, and understanding the model's sensitivity to hyperparameters.

How do I choose the right hyperparameters for my model?

Start by researching common hyperparameters for your specific model type, then experiment with different values to see their impact.

Is there a way to automate hyperparameter tuning?

Yes, you can use libraries like Optuna or Hyperopt that provide automated hyperparameter optimization techniques.

What metrics should I use to evaluate hyperparameter tuning results?

Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC, depending on your specific task.

Can you explain cross-validation in the context of hyperparameter tuning?

Cross-validation involves splitting your dataset into multiple subsets to ensure that your hyperparameter tuning is robust and not overfitting.