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Decision Tree MCQ

10 Conceptual Questions with In-Depth Technical Explanations

Solve MCQs on splitting criteria (Gini, Entropy), pruning, tree depth, and CART algorithms.

All Topics 📊Supervised Learning 🔍Unsupervised Learning 🎮Reinforcement Learning 🌓Semi-Supervised Learning ⚖️Bias and Variance 🌲Decision Tree 🌳Random Forest 📈Support Vector Machines (SVM)📍K-Nearest Neighbors (KNN)🎲Naive Bayes 🚀Gradient Boosting ⚡XGBoost 🎯AdaBoost 🤝Ensemble Learning 🎒Bagging and Boosting 🤖Machine Learning

Decision Tree Questions

Select an answer to reveal detailed technical explanation and formulas.

10 Questions

1

Which criterion measures the impurity or disorder of a node in classification decision trees?

2

What is Information Gain in decision tree algorithms?

3

What is the primary difference between ID3 and CART tree algorithms?

4

What is Cost-Complexity Pruning (minimal cost-complexity pruning)?

5

Why are unpruned decision trees notoriously prone to overfitting?

6

Which of the following is an orthogonal split in a standard decision tree?

7

How does a regression decision tree make a continuous value prediction for a test point that reaches a leaf node?

8

Are decision trees sensitive to monotonic feature scaling (e.g., standardizing or min-max normalization)?

9

What is a major limitation of standard single decision trees regarding stability?

10

Which hyperparameter directly restricts the maximum number of decision nodes along any path from the root to a leaf?

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About Decision Tree Interview Questions & Quiz

These Decision Tree multiple choice questions are curated to prepare engineers, researchers, and data science students for top technical interviews, machine learning certifications, and university exams.

Every MCQ incorporates core mathematical formulas, architectural tradeoffs, and practical algorithmic considerations. Practicing these questions guarantees a solid foundational and conceptual mastery of Decision Tree.