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from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
iris=load_iris()
X = iris.data
Y = iris.target
n = len(iris.data)
train = n // 2
2 0.88 0.96 0.92 23
avg / total 0.95 0.95 0.95 75
==MNIST==
from sklearn.datasets import fetch_mldatafetch_openml
from numpy import arange
import random
[[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)
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
<div class="oo-ui-panelLayout-scrollable" style="display: block; vertical-align:middle; height: auto; width: auto;">[[Файл:ICDARMSTD.png |x250px|center|thumb|Рисунок 26. ICDAR "Multi-lingual Scene Text". [https://arxiv.org/pdf/1812.05219.pdf Источник]]]</div>
<div class="oo-ui-panelLayout-scrollable" style="display: block; vertical-align:middle; height: auto; width: auto;">[[Файл:ICDARFS2.png |x250pxx150px|center|thumb|Рисунок 27. ICDAR "French Street Name Signs". [https://arxiv.org/pdf/1812.05219.pdf Источник]]]</div>
===ICDAR 2019===
