What are the best evaluation metrics for AI models?

What are the best evaluation metrics for AI models?

The best evaluation metrics depend on the type of model and task. For classification, accuracy, precision, recall, and F1 score are common. For regression, you might use mean absolute error (MAE) or root mean square error (RMSE).

Can you explain precision and recall?

Sure! Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances. High precision means fewer false positives, and high recall means fewer false negatives.

What metric should I use for an imbalanced dataset?

For imbalanced datasets, F1 score is often preferred as it balances precision and recall. You might also consider using area under the ROC curve (AUC-ROC) to evaluate model performance.

Are there any metrics specific to deep learning?

Yes, in deep learning, metrics like cross-entropy loss for classification tasks and mean squared error for regression are commonly used. Additionally, accuracy and AUC-ROC are also relevant.