Box 1: Microsoft.MachineLearningServices/workspaces/*/read

Reader role: Read-only actions in the workspace. Readers can list and view assets, including datastore credentials, in a workspace. Readers can't create or update these assets.

Box 2: Microsoft.MachineLearningServices/workspaces/*/write

If the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.

Box 3: Box 2: Microsoft.MachineLearningServices/workspaces/computes/*/delete

Box 4: Microsoft.MachineLearningServices/workspaces/computes/*/write

In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter τ), is a statistic used to measure the ordinal association between two measured quantities.

It is a supported method of the Azure Machine Learning Feature selection.

Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.

The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.

Note: A very natural question is: ‘Out of the individuals whom the model, how many were classified correctly (TP)?’

This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.