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 F1 score and when to 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.

How do I implement these metrics in Python?

You can use libraries like scikit-learn. For example, you can use sklearn.metrics.f1_score to calculate the F1 score after making predictions.

Are there any visualizations for these metrics?

Yes, you can use confusion matrices and ROC curves to visualize model performance. Libraries like Matplotlib and Seaborn can help with that.