Supervised vs unsupervised learning exam review
This flashcard set reviews the key concepts of supervised and unsupervised learning, including definitions, differences, examples, and applications in artificial intelligence and machine learning.
Quiz(32 questions)
1. What is the primary goal of unsupervised learning?
Terms in this Study Set(32)
Fundamentals of Supervised Learning(16)
What is supervised learning?
A type of machine learning where the model is trained on labeled data. It learns from input-output pairs.
What are labeled data?
Data that includes both input features and the corresponding output labels. Examples: (input: images, output: categories).
True or False: Supervised learning can be used for regression tasks.
True. It can predict continuous outcomes, like prices or temperatures.
Name two common algorithms for supervised learning.
- Linear Regression - Decision Trees
Fill in the blank: Supervised learning requires ______ data.
labeled
Cause → Effect: What happens when you train a supervised model?
The model learns to map inputs to outputs, improving prediction accuracy.
What is classification in supervised learning?
A task where the model predicts categorical labels. Example: email spam detection.
How does overfitting occur?
When a model learns noise in the training data too well, failing on new data.
What is the goal of supervised learning?
To develop a predictive model that generalizes well to unseen data.
True or False: Supervised learning can only be applied to structured data.
False. It can also handle semi-structured and unstructured data with appropriate preprocessing.
Comparison: Supervised vs. Unsupervised Learning
Supervised uses labeled data; Unsupervised uses unlabeled data.
What is a training set?
A subset of data used to train the model, consisting of input-output pairs.
How is a validation set used?
To tune hyperparameters and prevent overfitting during training.
What is the output of a regression model?
A continuous value. Example: predicting house prices in dollars.
Give an example of a supervised learning application.
Predicting customer churn based on historical data.
True or False: Supervised learning uses only categorical outputs.
False: Supervised learning can use categorical outputs (classification) and continuous outputs (regression), depending on the problem type.
Fundamentals of Unsupervised Learning(16)
What is unsupervised learning?
A type of machine learning where the model learns patterns from unlabelled data.
True or False: Unsupervised learning uses labeled data.
False. It relies on unlabelled data to find patterns or groupings.
Example of unsupervised learning.
Clustering customers into segments based on purchasing behavior without predefined labels.
What is clustering?
A technique in unsupervised learning that groups similar data points together.
Fill in the blank: The most common algorithm for clustering is ______.
K-means.
Difference between clustering and association.
Clustering groups data by similarity; association identifies rules of relationships between variables.
What are dimensionality reduction techniques?
Methods to reduce the number of features in a dataset while retaining essential information. Examples include PCA and t-SNE.
What is PCA?
Principal Component Analysis; a technique for reducing dimensionality by transforming to a new set of variables.
Cause → Effect: High-dimensional data → ______.
Increased computational complexity and overfitting risk.
What does anomaly detection do?
Identifies rare items or events in a dataset that differ significantly from the majority.
Example of anomaly detection.
Fraudulent transactions in banking systems detected as outliers in spending patterns.
True or False: Unsupervised learning can be used for data visualization.
True. It helps visualize high-dimensional data in lower dimensions, aiding understanding.
What is hierarchical clustering?
A method that builds a tree of clusters by merging or splitting them based on similarity.
Applications of unsupervised learning.
Market segmentation, social network analysis, and organizing computing clusters.
What is feature extraction?
The process of transforming raw data into a set of usable features for analysis.
True or False: Unsupervised learning guarantees accurate predictions.
False. It finds patterns, but predictions are not the primary goal.
Questions in this Study Set(32)
1. What is the primary goal of unsupervised learning?
2. What type of data does supervised learning utilize?
3. Which of the following is an example of clustering?
4. Which of the following algorithms is commonly used for classification tasks in supervised learning?
5. True or False: K-means is a clustering algorithm.
6. True or False: Supervised learning can address both classification and regression problems.
7. Which statement is NOT true about hierarchical clustering?
8. How does a model benefit from being trained on a training set?
9. What is the main use of dimensionality reduction techniques?
10. What is the main goal of supervised learning?
11. What does PCA stand for?
12. Which of the following is NOT a type of supervised learning task?
13. Which of the following is an application of unsupervised learning?
14. What happens during the validation phase of supervised learning?
15. What does anomaly detection focus on?
16. How can overfitting negatively impact a supervised learning model?
17. True or False: Unsupervised learning is only used in clustering tasks.
18. Fill in the blank: In supervised learning, the model learns from a series of ______.
19. Which algorithm is commonly used for clustering?
20. What indicates that a supervised learning model is performing well?
21. What is the relationship between high-dimensional data and overfitting?
22. Which of these is a common metric used to evaluate a supervised learning model’s performance?
23. Which of the following is NOT a technique for dimensionality reduction?
24. True or False: Supervised learning can only be applied to numerical data.
25. What is the purpose of feature extraction?
26. What is the purpose of a test set in supervised learning?
27. True or False: Unsupervised learning can improve interpretability of data.
28. Which scenario best exemplifies a supervised learning application?
29. What is the main difference between clustering and association?
30. In regression tasks, what type of output is expected?
31. Which of the following best describes the process of feature extraction?
32. Which of the following best describes the role of labeled data in supervised learning?
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