What are the best practices for model evaluation in Python machine learning?

What are the best practices for model evaluation in Python machine learning?

Some best practices include using cross-validation, selecting appropriate metrics, and ensuring data is split correctly.

Can you explain why cross-validation is important?

Cross-validation helps to assess how the results of a statistical analysis will generalize to an independent dataset.

What metrics should I use for evaluating a classification model?

Common metrics include accuracy, precision, recall, F1 score, and ROC-AUC, depending on your specific use case.

How do I handle imbalanced datasets during evaluation?

You can use techniques like resampling, adjusting class weights, or using metrics that are sensitive to class imbalance.

What tools or libraries can assist with model evaluation in Python?

Libraries like scikit-learn, TensorFlow, and Keras provide built-in functions for model evaluation and metrics.