ML Exp 4

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, y = iris.data, iris.target

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

clf = DecisionTreeClassifier(criterion="gini", max_depth=3, random_state=42)
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

plt.figure(figsize=(12,8))
plot_tree(clf, filled=True, feature_names=iris.feature_names, class_names=iris.target_names)
plt.show()

accuracy = accuracy_score(y_test, y_pred)
print("Model Accuracy on Test Data:", accuracy)

cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:\n", cm)

new_sample = [[6.0, 2.9, 4.5, 1.5]]
prediction = clf.predict(new_sample)
predicted_class = iris.target_names[prediction[0]]

print("New sample:", new_sample)
print("Predicted class:", predicted_class)