34 lines
927 B
Python
34 lines
927 B
Python
"""
|
|
================================
|
|
Digits Classification Exercise
|
|
================================
|
|
|
|
A tutorial exercise regarding the use of classification techniques on
|
|
the Digits dataset.
|
|
|
|
This exercise is used in the :ref:`clf_tut` part of the
|
|
:ref:`supervised_learning_tut` section of the
|
|
:ref:`stat_learn_tut_index`.
|
|
"""
|
|
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[: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()
|
|
|
|
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))
|