What are some common mistakes in model evaluation?

What are some common mistakes in model evaluation?

Some common mistakes include overfitting to the training data, ignoring validation sets, and not considering model bias.

Can you explain why overfitting is a problem?

Overfitting occurs when a model learns the training data too well, including noise, which leads to poor performance on unseen data.

What is the role of a validation set?

A validation set helps to tune model parameters and provides an unbiased evaluation of the model during training.

How can I avoid these mistakes?

You can avoid these mistakes by using cross-validation, monitoring performance metrics, and ensuring a proper train-test split.