decisionTree.py

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier , plot_tree
from sklearn.metrics import accuracy_score ,confusion_matrix
import matplotlib.pyplot as plt

iris= load_iris()

X=iris.data
y=iris.target

X_train , X_test , y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=42)

tree_clf = DecisionTreeClassifier(criterion="gini",max_depth=5,random_state=42)
tree_clf.fit(X_train,y_train)

y_pred = tree_clf.predict(X_test)
accuracy = accuracy_score(y_test,y_pred)
print("doğruluk:",accuracy)

conf_matrix = confusion_matrix(y_test,y_pred)
print("confusion matrix :")
print(conf_matrix)

plt.figure(figsize=(15,10))
plot_tree(tree_clf,filled=True,feature_names=iris.feature_names, class_names=list(iris.target_names))
plt.show()