Why is it important to know if something is a disease or not?
Why is it important to know if something is a disease or not?
Imagine you're a doctor and you need to quickly tell if a patient has a disease to decide on treatment.
In binary classification, we're trying to sort things into two groups. It's like deciding if a patient has a disease (positive) or doesn't (negative).
Example
Out of 100 patients, 80 have the disease and 20 don't. If we correctly identify 75 patients, we get 60 true positives and 15 true negatives.
Remember this
The F1 score helps us understand how well we're identifying true positives and true negatives, balancing both.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
Receiver operating characteristic
Ever wondered how doctors decide if a test really finds cancer?
to use F1 score: when classes are imbalanced and both FP and FN matter
Why might a sports team need a different score to judge their performance?
Evaluation of binary classifiers
Why can't we just use one score for everything?
to use AUC-ROC: comparing classifiers across all thresholds
Why can't we always trust a yes-or-no answer?
Model selection
Why do we sometimes choose simpler explanations over complex ones?
score matching does: learns the gradient of the log-density without normalizing
Ever wonder how we can compare apples and oranges fairly in studies?
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