페이지 정보

작성자스카이캐슬 조회 1회 작성일 2021-04-07 08:10:39 댓글 0


ROC and AUC, Clearly Explained!

ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the information from a ton of confusion matrices into a single, easy to interpret graph. This video walks you through how to create and interpret ROC graphs step-by-step. We then show how the AUC can be used to compare classification methods and, lastly, we talk about what to do when your data isn't as warm and fuzzy as it should be.

⭐ NOTE: When I code, I use Kite, a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I love it!\u0026utm_source=youtube\u0026utm_campaign=statquest\u0026utm_content=description-only

NOTE: This is the 2019.07.11 revision of a video published earlier.

NOTE: This video assumes you already know about
Confusion Matrices...

...Sensitivity and Specificity...

...and the example I work through is based on Logistic Regression, so it would help to understand the basics of that as well:

For a complete index of all the StatQuest videos, check out:

If you'd like to support StatQuest, please consider...
YouTube Membership:

...a cool StatQuest t-shirt or sweatshirt (USA/Europe):

...buying one or two of my songs (or go large and get a whole album!)

...or just donating to StatQuest!

Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:

0:00 Awesome song and introduction
0:48 Classifying samples with logistic regression
4:03 Creating a confusion matrices for different thresholds
7:12 ROC is an alternative to tons of confusion matrices
13:44 AUC to compare different models
14:28 False Positive Rate vs Precision
15:38 Summary of concepts

#statquest #ROC #AUC

ROC Curves and Area Under the Curve (AUC) Explained

An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others).

SUBSCRIBE to learn data science with Python:

JOIN the "Data School Insiders" community and receive exclusive rewards:

- Transcript and screenshots:
- Visualization:
- Research paper:

- Newsletter:
- Twitter:
- Facebook:
- LinkedIn:

#1.6. ROC 커브 (+ AUC, Precision, Recall)

Accuracy가 성능을 나타내는 전부는 아니란거 다들 알고 계시죠? 지난번엔 암환자 진단의 예를 통해 accuracy의 함정을 알아보고, precision과 recall에 대해서 설명을 했는데요,

이번에는 이와 함께 많이 쓰이는 개념 중 하나인 ROC curve에 대해서 알아보았습니다. 특히 메디컬 페이퍼에선 신뢰성 있는 판단을 위해 ROC를 많이 이용하는데요, ROC란 무엇이고 도대체 왜 쓰는 것일까요? ROC에 대해 한번 알아보시죠.

전체강의 재생목록:
페이스북 페이지:




등록된 댓글이 없습니다.

전체 3,454건 11 페이지
게시물 검색
Copyright © All rights reserved.  Contact :