Digits Classification Exercise

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

This exercise is used in the 分类 part of the 监督学习: 从高维观测中预测输出变量 section of the 用于科学数据处理的统计学习教程.


KNN score: 0.961111
LogisticRegression score: 0.933333


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,

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 0.437 seconds)

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