What is the difference between hyperparameter tuning and feature engineering?

What is the difference between hyperparameter tuning and feature engineering?

Hyperparameter tuning involves optimizing the parameters of a model to improve performance, while feature engineering focuses on creating new input features from existing data to enhance model accuracy.

Can you give me examples of hyperparameters and features?

Sure! Examples of hyperparameters include learning rate, number of trees in a random forest, and batch size. Features could be things like age, income, or derived metrics like total spending.

Which one is more important for model performance?

Both are crucial, but their importance can vary depending on the dataset and the model. Feature engineering often has a more significant impact on performance, especially with complex datasets.

How do I decide when to focus on one over the other?

Start with feature engineering to ensure your model has the best possible input data. Once you have a solid feature set, then focus on hyperparameter tuning to refine the model's performance.