You are working on an Azure Machine Learning experiment that uses four different logistic regression algorithms. You are evaluating the algorithms based on the data in the following table. Which…

The correct answer is C. Model 3. Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better. Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the…

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Question

You are working on an Azure Machine Learning experiment that uses four different logistic regression algorithms. You are evaluating the algorithms based on the data in the following table. Which model produces predictions that are the closest to the actual outcomes?

Options

AModel 1
BModel 2
CModel 3
DModel 4

How the community answered

(26 responses)

A
8% (2)
B
4% (1)
C
77% (20)
D
12% (3)

Explanation

Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better. Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean. Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction. https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-

Topics

#logistic regression#model evaluation#accuracy metrics#model comparison

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