What are common model validation errors in machine learning?

What are common model validation errors in machine learning?

Common model validation errors include overfitting, underfitting, data leakage, and incorrect evaluation metrics.

How can I prevent overfitting in my model?

You can prevent overfitting by using techniques like cross-validation, regularization, and pruning.

What is data leakage and how does it affect my model?

Data leakage occurs when information from outside the training dataset is used to create the model, leading to overly optimistic performance.

Can you explain the difference between training and validation datasets?

The training dataset is used to train the model, while the validation dataset is used to tune the model's parameters and evaluate its performance.