From 9a69552eb398a154a3c584d5db08400cd22b6995 Mon Sep 17 00:00:00 2001 From: Wayne Kwon Date: Mon, 24 Oct 2016 00:47:55 -0400 Subject: [PATCH 1/3] Submitting Questions.txt --- questions.txt | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 questions.txt diff --git a/questions.txt b/questions.txt new file mode 100644 index 0000000..8c9d3a1 --- /dev/null +++ b/questions.txt @@ -0,0 +1,4 @@ +1. General Trend: The Accuracy of the test increases as the percentage of data used for training increases. There are some points in the graph where the accuracy of the test actually decreases as the percentage of data used for training increases, but overall, we would say that these two are positively correlated. +2. The curve seems to be noisier in the lower percentage of data used for training. It may be because there are less observations being used for building the model so extreme outliers have a heavier effect on the accuracy of the test. +3. 100 trials made it a nice smooth curve. Any trials greater than 100 will make the curve smooth. +4. Decreasing C (by increasing the number that is being raised to the power of -10)results in better accuracy in the higher half of the percentage of data used, while increasing C results in better accuracy in the lower half of the percentage of data used. \ No newline at end of file From eeb317a1f80b5a47bcd8aeefe67280e5a03b9a25 Mon Sep 17 00:00:00 2001 From: Wayne Kwon Date: Mon, 24 Oct 2016 00:48:39 -0400 Subject: [PATCH 2/3] submitting toolbox machine learning python code --- machine_learning.py | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 machine_learning.py diff --git a/machine_learning.py b/machine_learning.py new file mode 100644 index 0000000..12badae --- /dev/null +++ b/machine_learning.py @@ -0,0 +1,38 @@ +""" Exploring learning curves for classification of handwritten digits """ + +import matplotlib.pyplot as plt +import numpy +from sklearn.datasets import * +from sklearn.cross_validation import train_test_split +from sklearn.linear_model import LogisticRegression + +data = load_digits() +print data.DESCR +num_trials = 50 +train_percentages = range(5,95,5) +test_accuracies = numpy.zeros(len(train_percentages)) + +# train a model with training percentages between 5 and 90 (see train_percentages) and evaluate +# the resultant accuracy. +# You should repeat each training percentage num_trials times to smooth out variability +# for consistency with the previous example use model = LogisticRegression(C=10**-10) for your learner +for i in range(len(train_percentages)): + accuracy = [] + for j in range(num_trials): + X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, train_size = train_percentages[i]/100.0) + + model = LogisticRegression(C=10**-10) + model.fit(X_train, y_train) + score = model.score(X_test, y_test) + + accuracy.append(score) + + test_accuracies[i] = numpy.average(accuracy) + + + +fig = plt.figure() +plt.plot(train_percentages, test_accuracies) +plt.xlabel('Percentage of Data Used for Training') +plt.ylabel('Accuracy on Test Set') +plt.show() From 05f94e1671d365fac584c738b50501818d2488cf Mon Sep 17 00:00:00 2001 From: Wayne Kwon Date: Mon, 24 Oct 2016 00:49:10 -0400 Subject: [PATCH 3/3] submitting example of the plot --- figure_1.png | Bin 0 -> 32261 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 figure_1.png diff --git a/figure_1.png b/figure_1.png new file mode 100644 index 0000000000000000000000000000000000000000..993014e49c0d21659e7fb40012273b936fe3a0ce GIT binary patch literal 32261 zcmeFZbySw!*DiVkf(nA7gs6mcqX;S>f=Z`!gD9djlG2Y!NH<7JcXufwp@?*spzLc+U;N1yFQzV{U{L2aI1aBS};}t z2S$M*A}bJtp9tp58(duYS^t!k4*gyC{r~^#|4XY8>^C)KM0|-)swYcBBSSf5YI!-2 zoI`zTu7s47bZv7}ttFUAt-|K1@ZO)I-Su{nb%E=!LF0$4bOWbOpYC{j`a*Y>29JC+ zpEL~(jpd5lV`HD&{%X_wKv5dIdUpEiWYa>$Y@CXi*xSKklTXkeWy(o-$#6&m z)=n^oWWw^MzcjD(SDIa{M{VOk;M zx?Ah(vL1Ihq@|^Ulc7&z=WL^v;K5Q09U2yVXz=G9Ztyz>N&n`dA)zjND=RC3SOx04 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