Machine Learning Basics Test (Free MCQs for Data Science)

1. Machine learning is primarily concerned with:

• (a) Writing SQL queries
• (b) Enabling computers to learn from data
• (c) Designing web pages
• (d) Performing manual calculations

2. Which of the following is a type of machine learning?

• (a) Supervised learning
• (b) Unsupervised learning
• (c) Reinforcement learning
• (d) All of the above

3. In supervised learning, models are trained using:

• (a) Only input features
• (b) Labeled data (input-output pairs)
• (c) Unlabeled data
• (d) Random guesses

4. In unsupervised learning, the algorithm:

• (a) Learns from labeled data
• (b) Finds patterns in unlabeled data
• (c) Uses reinforcement signals
• (d) Performs hypothesis testing

5. Which of the following is a common supervised learning task?

• (a) Classification
• (b) Clustering
• (c) Dimensionality reduction
• (d) Principal Component Analysis

6. Regression in machine learning is used to:

• (a) Predict categorical labels
• (b) Predict continuous numerical values
• (c) Cluster data points
• (d) Reduce dimensionality

7. Clustering is a type of:

• (a) Supervised learning
• (b) Unsupervised learning
• (c) Reinforcement learning
• (d) Deep learning only

8. Overfitting occurs when:

• (a) Model performs well on training but poorly on test data
• (b) Model performs equally well on all data
• (c) Model ignores features
• (d) Model has low complexity

9. Which technique can help reduce overfitting?

• (a) Cross-validation
• (b) Regularization
• (c) Pruning or dropout
• (d) All of the above

10. A feature in machine learning refers to:

• (a) Input variable used to predict output
• (b) Output label
• (c) Loss function
• (d) Learning rate

11. The goal of reinforcement learning is to:

• (a) Minimize loss function
• (b) Learn to take actions to maximize cumulative reward
• (c) Cluster similar data points
• (d) Compute statistical summaries

12. Accuracy in classification measures:

• (a) Ratio of correctly predicted labels to total labels
• (b) Sum of residual errors
• (c) Mean of feature values
• (d) Correlation coefficient

13. Which of the following is a commonly used evaluation metric for regression?

• (a) Mean Squared Error (MSE)
• (b) F1 Score
• (c) Accuracy
• (d) Precision

14. Dimensionality reduction techniques include:

• (a) Principal Component Analysis (PCA)
• (b) Linear Discriminant Analysis (LDA)
• (c) t-SNE
• (d) All of the above

15. A learning algorithm that improves performance as more data is provided is considered:

• (a) Data-driven
• (b) Machine learning
• (c) Supervised or unsupervised learning
• (d) All of the above