from sklearn.datasets import fetch_mldatafetch_openml from numpy import arange import random from sklearn.tree import DecisionTreeClassifier from sklearn import datasets, svm, metrics
[[Файл:Mnist-predict.png|мини|Рисунок 2. Результат работы классификатора]]
mnist = fetch_mldatafetch_openml('MNIST originalMNIST_784') indices = arange(len(mnist.data)) randidx = random.sample(list(indices), 500) mnist.data = mnist.data[randidx] mnist.target = mnist.target[randidx] X = mnist.data Y = mnist.target train = len(X)//2 clf = DecisionTreeClassifier(criterion="entropy", max_depth=5) clf.fit(X[:train], Y[:train]) expected = Y[train:] predicted = clf.predict(X[train:]) print("Classification report for classifier %s:\n%s\n" % (clf, metrics.classification_report(expected, predicted)))
digit precision recall f1-score support
