What are some common errors in AI modeling?

What are some common errors in AI modeling?

Common errors in AI modeling include overfitting, underfitting, data leakage, 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 prevent overfitting in my models?

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

What is data leakage and how can I avoid it?

Data leakage happens when information from outside the training dataset is used to create the model, leading to overly optimistic performance. Avoid it by ensuring that your training and test datasets are properly separated.