What are some effective techniques for evaluating AI models?

What are some effective techniques for evaluating AI models?

There are several techniques for evaluating AI models, including cross-validation, confusion matrix, and ROC curves.

Can you explain what cross-validation is?

Cross-validation is a technique used to assess how the results of a statistical analysis will generalize to an independent dataset. It involves partitioning the data into subsets, training the model on some subsets, and validating it on others.

What is a confusion matrix?

A confusion matrix is a table used to evaluate the performance of a classification model. It shows the true positive, true negative, false positive, and false negative rates.

How do ROC curves help in model evaluation?

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