lab8

 import numpy as np

import matplotlib.pyplot as plt

from sklearn.datasets import load_breast_cancer

from sklearn.model_selection import train_test_split

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import accuracy_score

from sklearn import tree


data = load_breast_cancer()

X_train, X_test, y_train, y_test = train_test_split(

    data.data, data.target, test_size=0.2, random_state=42

)


clf = DecisionTreeClassifier(random_state=42)

clf.fit(X_train, y_train)


accuracy = accuracy_score(y_test, clf.predict(X_test))

print(f"Accuracy: {accuracy * 100:.2f}%")


prediction_class = "Benign" if clf.predict([X_test[0]]) == 1 else "Malignant"

print(f"Predicted Class: {prediction_class}")


plt.figure(figsize=(12, 8))

tree.plot_tree(

    clf,

    filled=True,

    feature_names=data.feature_names,

    class_names=data.target_names

)

plt.title("Decision Tree - Breast Cancer Dataset")

plt.show()


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