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.

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[:.9 * n_samples]
y_train = y_digits[:.9 * n_samples]
X_test = X_digits[.9 * n_samples:]
y_test = y_digits[.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))

Total running time of the script: (0 minutes 0.000 seconds)

Download Python source code: digits_classification_exercise.py

Download IPython notebook: digits_classification_exercise.ipynb

Links:

http://scikit-learn.org/stable/auto_examples/exercises/digits_classification_exercise.html

doc_scikit_learn

2025-01-10 15:47:30

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