quiz.json

[
  {
    "id": "unsupervised-pre-1",
    "stage": "pre",
    "question": "What distinguishes unsupervised learning from supervised learning?",
    "options": [
      "Unsupervised learning uses more data",
      "Unsupervised learning has no labeled outputs -- the algorithm finds structure on its own",
      "Unsupervised learning only works with text data",
      "Unsupervised learning always produces better results"
    ],
    "correct": 1,
    "explanation": "In unsupervised learning, there are no labels. The algorithm discovers patterns, groupings, or structure in the data without being told what the correct output should be."
  },
  {
    "id": "unsupervised-pre-2",
    "stage": "pre",
    "question": "What does K-Means require you to specify before training?",
    "options": [
      "The exact cluster centers",
      "The number of clusters K",
      "The labels for each data point",
      "The distance metric to use"
    ],
    "correct": 1,
    "explanation": "K-Means requires the number of clusters K as input. It then iteratively assigns points to the nearest centroid and recomputes centroids until convergence."
  },
  {
    "id": "unsupervised-post-1",
    "stage": "post",
    "question": "K-Means fails on two interlocking half-moon shapes but DBSCAN succeeds. Why?",
    "options": [
      "DBSCAN uses more data than K-Means",
      "DBSCAN finds clusters based on density, so it can discover arbitrary shapes, while K-Means assumes spherical clusters",
      "DBSCAN always outperforms K-Means on every dataset",
      "K-Means cannot handle 2D data"
    ],
    "correct": 1,
    "explanation": "K-Means assigns points to the nearest centroid, producing spherical (convex) clusters. DBSCAN grows clusters from dense regions, discovering any shape as long as the cluster is connected by density."
  },
  {
    "id": "unsupervised-post-2",
    "stage": "post",
    "question": "What is the silhouette score measuring?",
    "options": [
      "The total number of clusters found",
      "How similar each point is to its own cluster compared to the nearest other cluster",
      "The speed of the clustering algorithm",
      "The percentage of outliers in the data"
    ],
    "correct": 1,
    "explanation": "Silhouette score = (b - a) / max(a, b), where a is mean intra-cluster distance and b is mean nearest-cluster distance. It ranges from -1 (wrong cluster) to +1 (well-clustered)."
  },
  {
    "id": "unsupervised-post-3",
    "stage": "post",
    "question": "How does a Gaussian Mixture Model differ from K-Means in its cluster assignments?",
    "options": [
      "GMM uses hard assignments where each point belongs to exactly one cluster",
      "GMM gives soft (probabilistic) assignments where each point has a probability of belonging to each cluster",
      "GMM does not use centroids at all",
      "GMM only works with one-dimensional data"
    ],
    "correct": 1,
    "explanation": "K-Means assigns each point to exactly one cluster (hard). GMM computes the probability that each point belongs to each Gaussian component (soft), and can model elliptical, overlapping clusters."
  }
]