MIT 15.071 The Analytics Edge, Spring 2017 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves
3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves Transcript and Lesson Notes
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Receiver Operator Characteristic (ROC) curves can help you decide which threshold value is the
Quick Summary
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Receiver Operator Characteristic (ROC) curves can help you decide which threshold value is the
Key Takeaways
- Review the core idea: MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Receiver Operator Characteristic (ROC) curves can help you decide which threshold value is the
- Understand how 15-071-the-analytics-edge-spring-2017 fits into 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves.
- Understand how color-coding fits into 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves.
- Understand how false positive rates fits into 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves.
- Understand how sensitivity fits into 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves.
Key Concepts
Full Transcript
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Receiver Operator Characteristic (ROC) curves can help you decide which threshold value is the best depending. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Lesson FAQs
What is 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves about?
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Receiver Operator Characteristic (ROC) curves can help you decide which threshold value is the
What key concepts are covered in this lesson?
The lesson covers 15-071-the-analytics-edge-spring-2017, color-coding, false positive rates, sensitivity.
What should I learn before 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves?
Review the previous lessons in MIT 15.071 The Analytics Edge, Spring 2017, then use the transcript and key concepts on this page to fill any gaps.
How can I practice after this lesson?
Practice by applying the main concepts: 15-071-the-analytics-edge-spring-2017, color-coding, false positive rates, sensitivity.
Does this lesson include a transcript?
Yes. The full transcript is visible on this page in indexable HTML sections.
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