What are some common mistakes in optimization?

What are some common mistakes in optimization?

Some common mistakes include overfitting, ignoring feature scaling, and not validating models properly.

Can you explain overfitting and why it's a problem?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern, leading to poor performance on new data.

How can I avoid overfitting in my models?

You can avoid overfitting by using techniques like cross-validation, regularization, and simplifying your model.

What about feature scaling? Why is it important?

Feature scaling ensures that all features contribute equally to the distance calculations in algorithms, improving model performance.