While deep diving to Azure Machine Learning Studio, I have been also exploring the performance metrics in Azure Machine Learning specifically for classification ML models.

**Confusion matrix** is one of the key performance metric to evaluate classification machine learning models. It shows the distribution of outcomes predicted by your model.

**Accuracy** is the proportion of true results to the total.

Accuracy = (True Positives + True Negatives) / TOTAL

**Precision** is the true results over all positive results.

Precision = True Positives / (True Positives + False Positives)

**Recall** is the total amount of relevant instances that were actually retrieved.

Recall = True Positives / (True Positives + False Negatives)

**F1 score ** is computed as the weighted average of precision and recall between 0 and 1, where the ideal F1 score value is 1.

**Area under the Receiver Operating Characteristic Curve** measures the quality of the model's predictions irrespective of what classification threshold is chosen. Closer to 1 is a good target.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-model

Let's use this in an example simplified Yes or No classification scenario where we have below results:

As for these metrics, you do not have to manually calculate them at all. You can use Evaluate model component in Azure ML Studio and check all results.