Machine Learning MCQ
Machine Learning MCQ Questions are designed to help you test and strengthen your understanding of key machine learning concepts. Covering a wide range of topics like supervised and unsupervised learning, algorithms, regression, classification, neural networks, and model evaluation, these multiple-choice questions cater to learners at all levels. Whether you're preparing for exams, interviews, or just want to enhance your machine learning skills, these MCQs will provide the perfect practice and help you stay updated on the latest trends in the field of machine learning.
Q1. What does ML stand for in computer science?
A. Multi Logic
B. Machine Learning
C. Meta Language
D. Matrix Loop
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Q2. Which type of learning uses labeled data?
A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. Deep learning
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Q3. Which learning method groups data without predefined labels?
A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. Semi-supervised
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Q4. Which algorithm is commonly used for classification problems?
A. K-Means
B. Linear Regression
C. Decision Tree
D. Apriori
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Q5. Which algorithm is commonly used for clustering?
A. Naive Bayes
B. K-Means
C. Logistic Regression
D. Linear Regression
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Q6. Which evaluation metric is commonly used for classification models?
A. Accuracy
B. Mean Squared Error
C. R-Squared
D. Silhouette Score
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Q7. Which ML algorithm is inspired by the human brain?
A. Decision Tree
B. Random Forest
C. Neural Network
D. Support Vector Machine
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Q8. Which is an example of supervised learning?
A. Spam email detection
B. Customer segmentation
C. Market basket analysis
D. Dimensionality reduction
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Q9. Which technique reduces the number of input features?
A. Clustering
B. Regression
C. Dimensionality Reduction
D. Classification
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Q10. Which of the following is a regression algorithm?
A. Linear Regression
B. Decision Tree Classifier
C. Naive Bayes
D. Apriori
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Q11. What does overfitting mean in ML?
A. Model performs poorly on training data
B. Model performs well only on training data but poorly on new data
C. Model performs equally on all data
D. Model ignores training data
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Q12. Which method helps to reduce overfitting?
A. Regularization
B. More hidden layers
C. Less training data
D. Ignoring validation
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Q13. Which type of learning is used in game playing by agents?
A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. Batch learning
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Q14. What is the purpose of a confusion matrix?
A. To store dataset
B. To check memory usage
C. To evaluate classification performance
D. To visualize clustering
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Q15. Which library is widely used for ML in Python?
A. NumPy
B. Matplotlib
C. Scikit-learn
D. Pandas
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Q16. Which algorithm is known as lazy learner?
A. KNN (K-Nearest Neighbors)
B. Decision Tree
C. Naive Bayes
D. Linear Regression
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Q17. Which ML model is called an ensemble method?
A. Linear Regression
B. Random Forest
C. K-Means
D. Naive Bayes
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Q18. Which activation function is commonly used in deep learning?
A. Step function
B. ReLU
C. Sigmoid
D. Both ReLU and Sigmoid
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Q19. Which term refers to dividing data into training and testing sets?
A. Validation
B. Data splitting
C. Cross-validation
D. Normalization
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Q20. Which metric is suitable for regression evaluation?
A. Accuracy
B. Precision
C. Recall
D. Mean Squared Error
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