What are some common evaluation metrics for machine learning models in Python?

What are some common evaluation metrics for machine learning models in Python?

Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC. Would you like to know more about any specific metric?

Can you explain the difference between precision and recall?

Sure! Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances. They are often used together to evaluate model performance.

How do I implement these metrics in Python?

You can use libraries like scikit-learn. For example, you can use precision_score and recall_score functions to calculate these metrics easily.

What is the F1 score and when should I use it?

The F1 score is the harmonic mean of precision and recall. It's useful when you need a balance between precision and recall, especially in imbalanced datasets.