What are some common mistakes in machine learning optimization?

What are some common mistakes in machine learning optimization?

Some common mistakes include overfitting, not tuning hyperparameters, and ignoring data preprocessing.

Can you explain overfitting and how to avoid it?

Overfitting occurs when a model learns the training data too well, including noise. To avoid it, use techniques like cross-validation, regularization, and pruning.

What about hyperparameter tuning? Why is it important?

Hyperparameter tuning is crucial because it helps find the best model settings, improving performance and generalization.

Are there tools that can help with hyperparameter tuning?

Yes, tools like Grid Search, Random Search, and libraries like Optuna and Hyperopt can assist in hyperparameter tuning.