The plots that decide. This lesson sits inside Module I — Logistic regression — of Classification, the course that anchors the Applied Machine Learning program. It is not a survey; it is the specific, working understanding of "ROC, PR, and the metrics" that the rest of the course assumes you carry forward.
- 01Define ROC, PR, and the metrics in the precise sense used across Classification.
- 02Recognize when ROC, PR, and the metrics is the correct lens for the situation in front of you, and when it is not.
- 03Apply ROC, PR, and the metrics to a concrete case drawn from Logistic regression, and defend the result in plain language.
- 04Connect ROC, PR, and the metrics to the adjacent lessons in this module without collapsing the distinctions between them.
The idea, stated plainly
The plots that decide. That single sentence is the whole lesson in compressed form. The rest of the reading unfolds it — what it means when the terms are taken seriously, where it comes from, and what work it does inside Classification. Read the sentence, then read it again after the sections below; it should carry more weight the second time.
Why it belongs in Logistic regression
Module I exists because the starting point. "ROC, PR, and the metrics" is one of the pillars of that module: without it, the later lessons either become memorization or lose their bite. Notice which earlier lessons this one leans on, and which later lessons will lean on it — the shape of the module is easier to see once you place this piece.
How the School of Artificial Intelligence faculty use it
In practice, working school of artificial intelligence professionals reach for this idea before they reach for a formula or a tool. It is a way of framing the problem so that the right question comes first. The mark of understanding is not that you can recite ROC, PR, and the metrics; it is that you catch yourself using it, unprompted, when the situation calls for it.
Common misreadings
The most frequent error is to treat ROC, PR, and the metrics as a slogan and skip the mechanics. The second most frequent is the opposite — treating the mechanics as the point, when the mechanics are only there to make the idea usable. Both errors collapse the same distinction, and both are correctable by returning to the one-line summary and asking what it actually claims.
A single-panel schematic. The center holds the phrase "ROC, PR, and the metrics". Four short lines radiate out to labels drawn from the module: the situation, the actors, the mechanism, and the constraint. The figure is meant to be redrawn by hand in under a minute.
A horizontal spine labeled with the module's lessons in order. The current lesson is marked; arrows point to the lessons immediately before and after it, indicating the direction of dependency. The visual makes the sequencing choices explicit.
A two-column diagram. The left column shows the concept as it is often misread — flattened into a slogan. The right column shows the same concept as Classification uses it, with the operational content restored. The gap between the columns is the lesson.
- ROC, PR, and the metrics is a working tool, not a slogan.
- Its meaning is set by the module it lives in: Logistic regression.
- Understanding is demonstrated by unprompted use in the correct situation.
- The adjacent lessons in this module are its natural context; read them together.
- 306 — Classification, Module I: Logistic regression — The parent module for this lesson. Re-read the module blurb after finishing the lesson.
- The Anabasis Academy — School of Artificial Intelligence, Applied Machine Learning — The wider program this lesson serves; the Certificate in Applied ML (Practitioner tier). credential ultimately certifies mastery of ideas like this one.