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Toolbox submission #18

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16 changes: 12 additions & 4 deletions learning_curve.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,8 +7,8 @@
from sklearn.linear_model import LogisticRegression

data = load_digits()
print data.DESCR
num_trials = 10
# print data.DESCR
num_trials = 100
train_percentages = range(5,95,5)
test_accuracies = numpy.zeros(len(train_percentages))

Expand All @@ -17,10 +17,18 @@
# 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
for i in range(len(train_percentages)):
trial_accuracies = numpy.zeros(num_trials)
for trial 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=10**-3)
model.fit(X_train, y_train)
trial_accuracies[trial] = model.score(X_test,y_test)
test_accuracies[i] = sum(trial_accuracies) / num_trials


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()
plt.show()
11 changes: 11 additions & 0 deletions questions.txt
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1. What is the general trend in the curve?
The accuracy of the test generally increases as the percentage of the data used to train the machine increases.

2. Are there parts of the curve that appear to be noisier than others? Why?
The curve seems to be consistently noisy at 10 trials.

3. How many trials do you need to get a smooth curve?
The curve started to smooth out at 2000 trials per percentage.

4. Try different values for C (by changing LogisticRegression(C=10**-10)). What happens? If you want to know why this happens, see this Wikipedia page as well as the documentation for LogisticRegression in scikit-learn.
As C gets larger, the noise in the curve decreases.