Digits Classification Exercise

A tutorial exercise regarding the use of classification techniques on the Digits dataset.

This exercise is used in the Classification part of the Supervised learning: predicting an output variable from high-dimensional observations section of the A tutorial on statistical-learning for scientific data processing.

Out:

KNN score: 0.961111
LogisticRegression score: 0.933333

print(__doc__)

from sklearn import datasets, neighbors, linear_model

digits = datasets.load_digits()
X_digits = digits.data / digits.data.max()
y_digits = digits.target

n_samples = len(X_digits)

X_train = X_digits[:int(.9 * n_samples)]
y_train = y_digits[:int(.9 * n_samples)]
X_test = X_digits[int(.9 * n_samples):]
y_test = y_digits[int(.9 * n_samples):]

knn = neighbors.KNeighborsClassifier()
logistic = linear_model.LogisticRegression(solver='lbfgs', max_iter=1000,
                                           multi_class='multinomial')

print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))
print('LogisticRegression score: %f'
      % logistic.fit(X_train, y_train).score(X_test, y_test))

Total running time of the script: ( 0 minutes 0.683 seconds)