TensorFlow Model Evaluation Metrics Guide

In TensorFlow, common model evaluation metrics include:

1. Accuracy: The proportion of correctly predicted samples to total samples.
2. Precision: the proportion of samples predicted as positive that are actually positive.
3. Recall: The proportion of actual positive samples that are correctly predicted as positive.
4. F1 Score is the harmonic mean of precision and recall, taking into account both the accuracy and completeness of the model.
5. AUC value (Area Under the Curve): The area under the ROC curve used to evaluate the performance of a binary classification model.
6. Confusion Matrix: A tool used to display a model’s predictions across different classes, including true positives, false positives, true negatives, and false negatives.
7. ROC curve (Receiver Operating Characteristic curve): A visualization tool that shows the trade-off between recall and false positive rate of a binary classification model at different thresholds.
8. Precision-Recall curve is used to evaluate the trade-off between precision and recall of a binary classification model at different thresholds.

These evaluation metrics can help us comprehensively assess the performance of the model in different aspects and choose the most suitable model for the task requirements.