cs401r_w2016:lab5
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| {{ :cs401r_w2016:lab5_confmat2.png?direct&300|}} | {{ :cs401r_w2016:lab5_confmat2.png?direct&300|}} | ||
| - | - The overall classification rate. For example, when I coded up Part 2, my classification error rate was 17.97%. When I coded up Part (3), my error rate was 3.80%. | + | - The overall classification rate. For example, when I coded up Part 2, my classification error rate was 17.97%. When I coded up Part (3), my error rate was <del>3.80%</del> 4.0%. (if you omit the factor of 1/2 in the exponent, you get 3.8%) |
| - A confusion matrix (see MLAPP pg. 183), or [[https://en.wikipedia.org/wiki/Confusion_matrix|this wikipedia article]]. A confusion matrix is a complete report of all of the different ways your classifier was wrong, and is much more informative than a single error rate; for example, a confusion matrix will report the number of times your classifier reported "3", when the true class was "8". You can report this confusion matrix either as a text table, or as an image. My confusion matrix is shown to the right; you can see that my classifier generally gets things right (the strong diagonal), but sometimes predicts "9" when the true class is "4" (for example). | - A confusion matrix (see MLAPP pg. 183), or [[https://en.wikipedia.org/wiki/Confusion_matrix|this wikipedia article]]. A confusion matrix is a complete report of all of the different ways your classifier was wrong, and is much more informative than a single error rate; for example, a confusion matrix will report the number of times your classifier reported "3", when the true class was "8". You can report this confusion matrix either as a text table, or as an image. My confusion matrix is shown to the right; you can see that my classifier generally gets things right (the strong diagonal), but sometimes predicts "9" when the true class is "4" (for example). | ||
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| ====Extra credit:==== | ====Extra credit:==== | ||
| - | Somewhere in the first 1000 training images is an outlier! Using the tools of kernel density estimation and anomaly detection, can you find it? | + | Somewhere in the first 1000 training images is an outlier! Using the tools of kernel density estimation and anomaly detection, can you find it? (To get credit for this, you cannot manually look for the outlier, you must automatically detect it; your notebook should contain the code you used to do this.) |
| - | If so, have your notebook display an image with the outlier, along with the index of the outlier. //Note: if you find the outlier, please don't tell other students which one it is!// | + | If so, have your notebook display an image with the outlier, along with the index of the outlier. |
| + | |||
| + | //**Note: if you find the outlier, please don't tell other students which one it is!**// | ||
| ---- | ---- | ||
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| - | The data that you will analyzing is the famous [[http://yann.lecun.com/exdb/mnist/|MNIST handwritten digits dataset]]. You can download some pre-processed MATLAB data files below: | + | The data that you will analyzing is the famous [[http://yann.lecun.com/exdb/mnist/|MNIST handwritten digits dataset]]. You can download some pre-processed MATLAB data files from the class Dropbox, or via direct links below: |
| - | [[http://hatch.cs.byu.edu/courses/stat_ml/mnist_train.mat|MNIST training data vectors and labels]] | + | [[https://www.dropbox.com/s/23vs1osykktxbqg/mnist_train.mat?dl=0|MNIST training data vectors and labels]] |
| - | [[http://hatch.cs.byu.edu/courses/stat_ml/mnist_test.mat|MNIST test data vectors and labels]] | + | [[https://www.dropbox.com/s/47dupql5jm9alc4/mnist_test.mat?dl=0|MNIST test data vectors and labels]] |
| These can be loaded using the scipy.io.loadmat function, as follows: | These can be loaded using the scipy.io.loadmat function, as follows: | ||
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| <code python> | <code python> | ||
| import matplotlib.pyplot as plt | import matplotlib.pyplot as plt | ||
| - | plt.imshow( X.reshape(28,28).T, interpolation='nearest', cmap=matplotlib.cm.gray) | + | plt.imshow( X.reshape(28,28).T, interpolation='nearest', cmap="gray") |
| </code> | </code> | ||
cs401r_w2016/lab5.1452880806.txt.gz · Last modified: 2021/06/30 23:40 (external edit)
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