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

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(0.9 * n_samples)]
y_train = y_digits[: int(0.9 * n_samples)]
X_test = X_digits[int(0.9 * n_samples) :]
y_test = y_digits[int(0.9 * 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 0.341 seconds)

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