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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
X_digits, y_digits = datasets.load_digits(return_X_y=True)
X_digits = X_digits / X_digits.max()
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(max_iter=1000)
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 0.607 seconds)
Estimated memory usage: 8 MB
