From aecbc4821c6feba7f87c27a7788fee4b2e54dac3 Mon Sep 17 00:00:00 2001 From: Jason Lee Date: Wed, 9 Nov 2016 23:44:43 -0500 Subject: [PATCH 1/5] Turning in Machine Learning ToolBox --- learning_curve.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/learning_curve.py b/learning_curve.py index 2364f2c..92e0283 100755 --- a/learning_curve.py +++ b/learning_curve.py @@ -17,7 +17,14 @@ # 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 -# TODO: your code here +model = LogisticRegression(C=10**-50) +for x in train_percentages: + summing = [] + for i in range(num_trials): + x_train, x_test, y_train, y_test = train_test_split(data.data, data.target, train_size=float(x) * .01) + model.fit(x_train, y_train) + summing.append(model.score(x_test, y_test)) + test_accuracies[(x-1)/5] = float(sum(summing))/len(summing) fig = plt.figure() plt.plot(train_percentages, test_accuracies) From 0321354da53b207c09609680b555ee17da57d4fa Mon Sep 17 00:00:00 2001 From: Jason Lee Date: Wed, 9 Nov 2016 23:44:53 -0500 Subject: [PATCH 2/5] Turning in questions --- question.txt | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 question.txt diff --git a/question.txt b/question.txt new file mode 100644 index 0000000..e8e14e4 --- /dev/null +++ b/question.txt @@ -0,0 +1,4 @@ +1. The general trend of the curve is upwards. It seems like as accuracy increases, the percentage of data used for training increases, yet the curve appears to be leveled off at higher percentages. +2. It seems like the curve appears to be noisier in the range of 55 to 80 percent of data used for training. The reason would be that there are less observation in the model to continuously measure the accuracy of the model. +3. About 100 trials to get the a smooth curve. +4. When I tried with C=10**-1 (the larger value than the one that I used), the curve rapidly increases and normalizes at a high percentage. From 4344d08dab8518d6ed9cb5c507c2f30a4cb2fc75 Mon Sep 17 00:00:00 2001 From: Jason Lee Date: Wed, 9 Nov 2016 23:45:10 -0500 Subject: [PATCH 3/5] Turning in questions --- 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..e8e14e4 --- /dev/null +++ b/questions.txt @@ -0,0 +1,4 @@ +1. The general trend of the curve is upwards. It seems like as accuracy increases, the percentage of data used for training increases, yet the curve appears to be leveled off at higher percentages. +2. It seems like the curve appears to be noisier in the range of 55 to 80 percent of data used for training. The reason would be that there are less observation in the model to continuously measure the accuracy of the model. +3. About 100 trials to get the a smooth curve. +4. When I tried with C=10**-1 (the larger value than the one that I used), the curve rapidly increases and normalizes at a high percentage. 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The general trend of the curve is upwards. It seems like as accuracy increases, the percentage of data used for training increases, yet the curve appears to be leveled off at higher percentages. -2. It seems like the curve appears to be noisier in the range of 55 to 80 percent of data used for training. The reason would be that there are less observation in the model to continuously measure the accuracy of the model. -3. About 100 trials to get the a smooth curve. -4. When I tried with C=10**-1 (the larger value than the one that I used), the curve rapidly increases and normalizes at a high percentage.