Binary Classification AUC Score
Question
A company has started using the AWS machine learning service and is using Binary classification for its model.
After the initial evaluation, the AUC is showing a score of 0.51
What does this indicate?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer - C.
With the Binary classification model, the default cutoff between a positive or negative is 0.5
Any score close to 1 means that the model is accurate.
Having a score of 0.5 is more like taking a guess on whether the result is positive or negative.
For more information on binary classification, please refer to the below URL.
https://docs.aws.amazon.com/machine-learning/latest/dg/binary-classification.html/answer/imgckeditor_awsbdpt1uq56-score_distribution_for_a_binary_classification_model.png)
The AUC (Area Under the Curve) is a common metric used to evaluate the performance of a binary classification model. It measures the model's ability to distinguish between positive and negative classes. The AUC ranges from 0 to 1, with a score of 0.5 indicating random guessing and a score of 1 indicating perfect classification.
In this scenario, the AUC score is 0.51, which is only slightly above the random guessing threshold. This suggests that the model's performance is not much better than guessing.
Therefore, the correct answer would be option C - The evaluation is more like taking a guess on the result. This score is considered low and indicates that the model needs further improvement.
It is not advisable to jump to option D, which is to change the classification model, based on just one evaluation metric. It is important to conduct a thorough analysis of the model's performance and assess which aspects of the model need improvement before deciding to change the model.
Overall, it is crucial to monitor the model's performance continuously, conduct regular evaluations, and make necessary improvements to ensure that it is performing optimally.