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.
print(__doc__) from sklearn import datasets, neighbors, linear_model digits = datasets.load_digits() X_digits = digits.data y_digits = digits.target n_samples = len(X_digits) X_train = X_digits[:.9 * n_samples] y_train = y_digits[:.9 * n_samples] X_test = X_digits[.9 * n_samples:] y_test = y_digits[.9 * n_samples:] knn = neighbors.KNeighborsClassifier() logistic = linear_model.LogisticRegression() 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 0.000 seconds)
Download Python source code:
digits_classification_exercise.py
Download IPython notebook:
digits_classification_exercise.ipynb
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