Decision trees vs. neural networks
Build a decision tree classifier to classify income levels based on multiple features including age, education level, and hours worked per week, and extract the learned rules that explain the decision. Then, compare its performance with an MLPClassifier trained on the same data.
X_train, X_test, y_train, and y_test are pre-loaded for you. The accuracy_score and export_text functions are also imported for you.
Latihan ini merupakan bagian dari kursus
Explainable AI in Python
Latihan interaktif langsung praktik
Cobalah latihan ini dengan melengkapi kode contoh ini.
model = DecisionTreeClassifier(random_state=42, max_depth=2)
model.fit(X_train, y_train)
# Extract the rules
rules = ____
print(rules)
y_pred = model.predict(X_test)
# Compute accuracy
accuracy = ____
print(f"Accuracy: {accuracy:.2f}")