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← Confusion Matrix for Eigenfaces - Intro to Machine Learning

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Showing Revision 6 created 05/24/2016 by Udacity Robot.

  1. So talk about confusion matrices in different context, you might
  2. remember the work that Katie showed you on principle component analysis, on PCA.
  3. Were we looked at seven different, I guess white male politicians from
  4. George Bush to Gerhard Schroeder, and we ran an eigenface analysis.
  5. Extracting the principle components of this data set, and then re-use
  6. the eigenfaces to re-map new faces to names in order to identify people.
  7. So what I'm going to do now, I won't drag you
  8. through the same PCA example again, so let's do away with those faces.
  9. But instead what I'll do is, I give you a typical output and
  10. we're going to study the output using confusion matrices.
  11. So Katie was so nice to run a PCA on the faces of those politicians and
  12. take the resulting features, put it into a support vector machine and
  13. then go through the data and
  14. count how often any of those people were predicted correctly or misclassified.
  15. And just to confuse everybody, in this example,
  16. we follow the convention of putting the true names, the true class labels,
  17. on the left, and the predicted ones on top.
  18. So, for example, this number one over here was truly Donald Rumsfeld, but
  19. was mistaken to be Colin Powell.
  20. And the way I know it's Colin Powell was the same names over here,
  21. from Ariel Sharon to Tony Blair, apply to the columns over here.
  22. Ariel Sharon's on the left and Tony Blair's on the right.
  23. So we ask you a few questions now.
  24. First, a simple one.
  25. Which of those seven politicians was most frequent in our dataset?