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Turning in my machine learning toolbox #21

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15 changes: 13 additions & 2 deletions learning_curve.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
from sklearn.linear_model import LogisticRegression

data = load_digits()
print data.DESCR
# print data.DESCR
num_trials = 10
train_percentages = range(5,95,5)
test_accuracies = numpy.zeros(len(train_percentages))
Expand All @@ -16,8 +16,19 @@
# 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
test_accuracies=[]
for j in train_percentages:
train_size = j / 100.0
cumulative_train = 0
cumulative_test = 0
for i in range(10): #do 10 trials
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, train_size=train_size)
model = LogisticRegression(C=10**-20)
model.fit(X_train, y_train)
cumulative_train = model.score(X_train,y_train) + cumulative_train
cumulative_test = model.score(X_test,y_test) + cumulative_test
test_accuracies.append(cumulative_test)

# TODO: your code here

fig = plt.figure()
plt.plot(train_percentages, test_accuracies)
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7 changes: 7 additions & 0 deletions questions.txt
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1. It seems like a logarithmic graph - it is increasing but has a downward curve.

2. It seems to be noisiest in the 40-60 range. I would imagine because this is the area where the training set becomes larger than the testing set.

3. It seems to smooth out upwards of 75-100 trials.

4. With a much larger C (10^-1, 10), the graph rises sharply in the 20-30% range, then mellows out a lot. With a very small C (10^-20), the graph shoots up quickly at the beginning then falls down after about 25%.