# 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 = 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 0.067 seconds)

Related examples

Pipelining: chaining a PCA and a logistic regression

Pipelining: chaining a PCA and a logistic regression

Compare Stochastic learning strategies for MLPClassifier

Compare Stochastic learning strategies for MLPClassifier

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

SVM Exercise

SVM Exercise

Restricted Boltzmann Machine features for digit classification

Restricted Boltzmann Machine features for digit classification

Gallery generated by Sphinx-Gallery