From Confusion Matrix to ROC curve: or the link between machine learning and epidemiology
Previous topics
1. Introduction to statistics: The (small) Big Picture or how to solve 95% of statistical problems
2. Linear regression vs. Statistical tests ⚔ who wins?
3. Logistic regression: or what is the probability of success?
Why do we need ROC curves and confusion matrices?
For assessing the predictive accuracy of the classifier (i.e. logistic regression) or for assessing the accuracy of medical tests and calculating lot’s of medical metrics (i.e. prevalence). Since confusion matrix works for both, classification and medicine, there is a nice link between machine learning and epidemiology.
This is the last lecture in my coarse “Statistics for non statisticians”. Critical comments and wishes for the future lectures are highly appreciated!
Enjoy! 🥳
If you want to see only slides, go to: https://yury-zablotski.netlify.app/po...
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