Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

Note

Go to the end to download the full example code or to run this example in your browser via Binder

# 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.

    KNN score: 0.961111
    LogisticRegression score: 0.933333

    from sklearn import datasets, linear_model, neighbors

    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 0.109 seconds)

Gallery generated by Sphinx-Gallery