You create a binary classification model. You need to evaluate the model performance. Which two metrics can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is…

The correct answer is B. precision C. accuracy. 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…

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Question

You create a binary classification model. You need to evaluate the model performance. Which two metrics can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Options

Arelative absolute error
Bprecision
Caccuracy
Dmean absolute error
Ecoefficient of determination

How the community answered

(55 responses)

A
2% (1)
B
93% (51)
D
2% (1)
E
4% (2)

Explanation

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. https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

Topics

#Model Evaluation#Classification Metrics#Binary Classification#Accuracy

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