FAQ: Logistic Regression II - ROC Curve and AUC

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This community-built FAQ covers the “ROC Curve and AUC” exercise from the lesson “Logistic Regression II”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Data Scientist: Machine Learning Specialist

Machine Learning: Logistic Regression

FAQs on the exercise ROC Curve and AUC

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2

  1. What is DummyClassifier ?
    Is it just the threshold of 0.5?

  2. AUC = Numeric area from 0to1 under the curve ? Don’t we always will have area from 0 to 1 ?
    “A value close to one is a near-perfect classifier” is it at threshold = 0.32 on our chart ?

3

The Dummy Classifier shows where the TPR (True Positive Rate) == FPR (False Positive Rate) Any plot point on the line means that the amount of correctly classified samples is the same number of incorrectly classified samples.

If a point is to the left of the Dummy Classifier the amount of correct classified samples is more than the amount of incorrect classifications

4

y_true and y_score in the description of Step 1 probably come from the documentation of roc_curve. I think it would be better to describe them like ‘the true binary label and the predicted probability of the positive class’ as in Step 2.