What are some effective model evaluation techniques in machine learning?

What are some effective model evaluation techniques in machine learning?

Some effective techniques include cross-validation, confusion matrix, ROC curves, and precision-recall metrics.

Can you explain cross-validation in more detail?

Sure! Cross-validation involves partitioning the data into subsets, training the model on some subsets, and validating it on others to ensure it generalizes well.

What is a confusion matrix and how is it used?

A confusion matrix is a table that summarizes the performance of a classification model by showing true vs. predicted classifications.

How do I interpret ROC curves?

ROC curves plot the true positive rate against the false positive rate, helping to visualize the trade-off between sensitivity and specificity.