3.6.10.9. Compare classifiers on the digits data

Compare the performance of a variety of classifiers on a test set for the digits data.

Out:

LinearSVC: 0.9341800269333108
GaussianNB: 0.8332741681010101
KNeighborsClassifier: 0.9804562804949924
------------------
LinearSVC(loss='hinge'): 0.9294570108037394
LinearSVC(loss='squared_hinge'): 0.9341371852581549
-------------------
KNeighbors(n_neighbors=1): 0.9913675218842191
KNeighbors(n_neighbors=2): 0.9848442068835102
KNeighbors(n_neighbors=3): 0.9867753449543099
KNeighbors(n_neighbors=4): 0.9803719053818863
KNeighbors(n_neighbors=5): 0.9804562804949924
KNeighbors(n_neighbors=6): 0.9757924194139573
KNeighbors(n_neighbors=7): 0.9780645792142071
KNeighbors(n_neighbors=8): 0.9780645792142071
KNeighbors(n_neighbors=9): 0.9780645792142071
KNeighbors(n_neighbors=10): 0.9755550897728812

from sklearn import model_selection, datasets, metrics
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier

digits = datasets.load_digits()
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y,
                            test_size=0.25, random_state=0)

for Model in [LinearSVC, GaussianNB, KNeighborsClassifier]:
    clf = Model().fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    print('%s: %s' %
          (Model.__name__, metrics.f1_score(y_test, y_pred, average="macro")))

print('------------------')

# test SVC loss
for loss in ['hinge', 'squared_hinge']:
    clf = LinearSVC(loss=loss).fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    print("LinearSVC(loss='{0}'): {1}".format(loss,
          metrics.f1_score(y_test, y_pred, average="macro")))

print('-------------------')

# test the number of neighbors
for n_neighbors in range(1, 11):
    clf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    print("KNeighbors(n_neighbors={0}): {1}".format(n_neighbors,
        metrics.f1_score(y_test, y_pred, average="macro")))

Total running time of the script: ( 0 minutes 1.025 seconds)