Turning in my last toolbox

learning_curve.py

@@ -7,20 +7,21 @@
 from sklearn.linear_model import LogisticRegression
 
 data = load_digits()
-print data.DESCR
-num_trials = 10
+num_trials = 100
 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)):
+	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]))
+		model = LogisticRegression(C=5**-5)
+		model.fit(X_train, y_train)
+		test_accuracies[i] = test_accuracies[i]+model.score(X_test,y_test)
 
-# TODO: your code here
 
 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()
+

questions.txt

@@ -0,0 +1,4 @@
+1. The general trend is upward.
+2. The beginning and end of the curve appear to be noisiest. This is likely because testing and training both require a certain amount of data to work well, so when almost all the data is being used for one or the other the curve can get messed up.
+3.The curve was pretty smooth around 5000 trials. A good way to smooth the curve even more at large numbers of trials was to change the step size in the percentages from 5 to 1.
+4. Higher C values emphasize noise, and lower C values smooth.