2. Exposure to weather can lead to a reduction in the performance and efficiency of photovoltaic systems. Researchers have used machine learning to develop a test which categorises solar panels as defective or non-defective, based on textural features of the solar cells extracted from thermal imaging [2]. In a sample of 260 solar panels, 130 were classified as defective by registered electrical power engineers. Of these, the machine learning test was able to correctly identify 126 defective solar panels. The test correctly classified all non-defective solar panels. (a) Construct a binary classification table for the outcomes of the machine learning test, and calculate the sensitivity, specificity and accuracy of the test, showing all working.

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
icon
Related questions
Question
2. Exposure to weather can lead to a reduction in the performance and efficiency of photovoltaic
systems. Researchers have used machine learning to develop a test which categorises solar panels
as defective or non-defective, based on textural features of the solar cells extracted from thermal
imaging [2].
In a sample of 260 solar panels, 130 were classified as defective by registered electrical power
engineers. Of these, the machine learning test was able to correctly identify 126 defective solar
panels. The test correctly classified all non-defective solar panels.
(a) Construct a binary classification table for the outcomes of the machine learning test, and
calculate the sensitivity, specificity and accuracy of the test, showing all working.
Transcribed Image Text:2. Exposure to weather can lead to a reduction in the performance and efficiency of photovoltaic systems. Researchers have used machine learning to develop a test which categorises solar panels as defective or non-defective, based on textural features of the solar cells extracted from thermal imaging [2]. In a sample of 260 solar panels, 130 were classified as defective by registered electrical power engineers. Of these, the machine learning test was able to correctly identify 126 defective solar panels. The test correctly classified all non-defective solar panels. (a) Construct a binary classification table for the outcomes of the machine learning test, and calculate the sensitivity, specificity and accuracy of the test, showing all working.
Expert Solution
steps

Step by step

Solved in 2 steps with 2 images

Blurred answer
Similar questions
  • SEE MORE QUESTIONS
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman