Explore the essential metrics—Accuracy, Precision, and Recall—used in Azure ML for assessing classification models. Learn how these metrics shape model evaluation and performance.
Table of Contents
Question
What metrics does Azure ML use for the evaluation of the classification models?
Select all that apply.
A. Root Mean Squared Error (RMSE)
B. Accuracy
C. Number of Points
D. Precision
E. Combined Evaluation
F. Coefficient of Determination
G. Recall
Answer
B. Accuracy
D. Precision
G. Recall
Explanation
The metrics used by Azure ML for evaluating classification models are:
B. Accuracy: Measures the overall correctness of predictions made by the model.
D. Precision: Assesses the accuracy of positive predictions, indicating how many retrieved instances are relevant.
G. Recall: Gauges the model’s ability to identify all relevant instances, measuring how many relevant instances were retrieved.
Azure ML uses model evaluation for the measurement of the trained model accuracy. For classification models, the Evaluate Model module provides the following five metrics: Accuracy, Precision, Recall, F1 Score, and Area Under Curve (AUC).
Option B is correct. Accuracy is the classification model evaluation metrics. It represents how many cases the model predicted right proportionally to the total number of cases.
Option D is correct. Precision is the classification model evaluation metrics. It represents how many positive cases are predicted right.
Option G is correct. Recall is the classification model evaluation metrics. It represents how many positive cases that model predicted are predicted right.
Option A is incorrect. Root Mean Squared Error (RMSE) is the regression model evaluation metrics and is not the classification model evaluation metric. It represents the square root from the squared mean of the errors between predicted and actual values.
Option C is incorrect. Number of Points is the clustering model evaluation metrics and is not the classification model evaluation metric.
Option E is incorrect. Combined Evaluation is the clustering model evaluation metrics and is not the classification model evaluation metric.
Option F is incorrect. Coefficient of determination or R2 is the regression model evaluation metrics and is not the classification model evaluation metric.
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