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decision_tree_classifier.py
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import pandas as pd
import graphviz
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.metrics import precision_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
x_training, x_test, y_training, y_test = train_test_split(x, y, test_size = 0.2)
model = DecisionTreeClassifier(criterion="entropy", max_depth=10)
model.fit(x_training, y_training)
y_pred = model.predict(x_test)
print(confusion_matrix(y_test, predictions))
print(classification_report(y_test, y_pred))
print('Точность обучающей выборки:', model.score(x_training, y_training))
print('Точность тестовой выборки:', accuracy_score(y_test, y_pred))
dot_data = export_graphviz(model, out_file=None, class_names=['Был', 'Не был'],
feature_names=dataset.drop("dental caries", axis=1).columns, impurity=False, filled=True)
graph = graphviz.Source(dot_data)
graph.render('tree')
graph