Evaluation Criteria

Evaluation criteria are metrics used to assess the performance of machine learning models.

Evaluation criteria are used throughout the model development and deployment process to ensure that the model performs well on the given task. They help in comparing different models and selecting the best one for a specific problem.

For classification tasks, common evaluation criteria include F-Score, Accuracy, ROC-AUC, and Matthews Correlation Coefficient for imbalanced data, as well as Precision and Recall. These metrics are calculated based on a confusion matrix. For example, in a classification task, Accuracy measures the proportion of correctly predicted instances, while Precision and Recall provide insights into the model’s performance on positive instances.

For generative tasks and other specialized tasks, different criteria and benchmark datasets are used to evaluate performance.

Evaluation criteria provide an intuition of what is considered “good” performance. However, their interpretation must compare the obtained metrics with reasonable baselines. Those can include human-level performance or comparing the model’s performance with simpler models like Zero R, Random Forest, or comparing a large language model with a smaller model.

Understanding and selecting appropriate evaluation criteria is crucial for developing effective machine learning models.