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Quiz Part 2: Data Preprocessing and Classical Machine Learning
Chapter 5: Unsupervised Learning Techniques
- What is the main difference between supervised and unsupervised learning?
- a) Supervised learning requires labeled data, whereas unsupervised learning does not
- b) Unsupervised learning works only with numerical data
- c) Supervised learning groups data into clusters
- d) Both techniques require labeled data
- Which algorithm is a density-based clustering method?
- a) K-Means
- b) Hierarchical Clustering
- c) DBSCAN
- d) t-SNE
- Which of the following best describes Principal Component Analysis (PCA)?
- a) A supervised learning algorithm for classification
- b) A dimensionality reduction technique that preserves variance
- c) A method for detecting outliers in the data
- d) An algorithm for optimizing hyperparameters
- What does the Silhouette Score measure in clustering?
- a) The overall accuracy of clustering
- b) The separation between clusters
- c) How similar a data point is to its own cluster compared to other clusters
- d) The density of clusters
- What is the key advantage of UMAP over t-SNE?
- a) UMAP preserves only local structure, while t-SNE preserves both local and global structure
- b) UMAP is faster and more scalable than t-SNE, making it more suitable for larger datasets
- c) t-SNE performs better on high-dimensional data
- d) UMAP does not require parameter tuning, while t-SNE does
Chapter 5: Unsupervised Learning Techniques
- What is the main difference between supervised and unsupervised learning?
- a) Supervised learning requires labeled data, whereas unsupervised learning does not
- b) Unsupervised learning works only with numerical data
- c) Supervised learning groups data into clusters
- d) Both techniques require labeled data
- Which algorithm is a density-based clustering method?
- a) K-Means
- b) Hierarchical Clustering
- c) DBSCAN
- d) t-SNE
- Which of the following best describes Principal Component Analysis (PCA)?
- a) A supervised learning algorithm for classification
- b) A dimensionality reduction technique that preserves variance
- c) A method for detecting outliers in the data
- d) An algorithm for optimizing hyperparameters
- What does the Silhouette Score measure in clustering?
- a) The overall accuracy of clustering
- b) The separation between clusters
- c) How similar a data point is to its own cluster compared to other clusters
- d) The density of clusters
- What is the key advantage of UMAP over t-SNE?
- a) UMAP preserves only local structure, while t-SNE preserves both local and global structure
- b) UMAP is faster and more scalable than t-SNE, making it more suitable for larger datasets
- c) t-SNE performs better on high-dimensional data
- d) UMAP does not require parameter tuning, while t-SNE does
Chapter 5: Unsupervised Learning Techniques
- What is the main difference between supervised and unsupervised learning?
- a) Supervised learning requires labeled data, whereas unsupervised learning does not
- b) Unsupervised learning works only with numerical data
- c) Supervised learning groups data into clusters
- d) Both techniques require labeled data
- Which algorithm is a density-based clustering method?
- a) K-Means
- b) Hierarchical Clustering
- c) DBSCAN
- d) t-SNE
- Which of the following best describes Principal Component Analysis (PCA)?
- a) A supervised learning algorithm for classification
- b) A dimensionality reduction technique that preserves variance
- c) A method for detecting outliers in the data
- d) An algorithm for optimizing hyperparameters
- What does the Silhouette Score measure in clustering?
- a) The overall accuracy of clustering
- b) The separation between clusters
- c) How similar a data point is to its own cluster compared to other clusters
- d) The density of clusters
- What is the key advantage of UMAP over t-SNE?
- a) UMAP preserves only local structure, while t-SNE preserves both local and global structure
- b) UMAP is faster and more scalable than t-SNE, making it more suitable for larger datasets
- c) t-SNE performs better on high-dimensional data
- d) UMAP does not require parameter tuning, while t-SNE does
Chapter 5: Unsupervised Learning Techniques
- What is the main difference between supervised and unsupervised learning?
- a) Supervised learning requires labeled data, whereas unsupervised learning does not
- b) Unsupervised learning works only with numerical data
- c) Supervised learning groups data into clusters
- d) Both techniques require labeled data
- Which algorithm is a density-based clustering method?
- a) K-Means
- b) Hierarchical Clustering
- c) DBSCAN
- d) t-SNE
- Which of the following best describes Principal Component Analysis (PCA)?
- a) A supervised learning algorithm for classification
- b) A dimensionality reduction technique that preserves variance
- c) A method for detecting outliers in the data
- d) An algorithm for optimizing hyperparameters
- What does the Silhouette Score measure in clustering?
- a) The overall accuracy of clustering
- b) The separation between clusters
- c) How similar a data point is to its own cluster compared to other clusters
- d) The density of clusters
- What is the key advantage of UMAP over t-SNE?
- a) UMAP preserves only local structure, while t-SNE preserves both local and global structure
- b) UMAP is faster and more scalable than t-SNE, making it more suitable for larger datasets
- c) t-SNE performs better on high-dimensional data
- d) UMAP does not require parameter tuning, while t-SNE does