quiz.json

[
  {
    "id": "featsel-pre-1",
    "stage": "pre",
    "question": "Why can adding more features actually make a model perform worse?",
    "options": [
      "More features always improve model accuracy",
      "Irrelevant features add noise, increase overfitting risk, and dilute the signal from useful features",
      "Models have a hard limit on the number of features they can accept",
      "More features make the model run out of memory"
    ],
    "correct": 1,
    "explanation": "Irrelevant features give the model opportunities to overfit on noise in the training data. They increase dimensionality, making the data sparser and distances less meaningful (curse of dimensionality)."
  },
  {
    "id": "featsel-pre-2",
    "stage": "pre",
    "question": "What is the key difference between filter and wrapper feature selection methods?",
    "options": [
      "Filter methods use a model to evaluate features; wrapper methods use statistics",
      "Filter methods score features using statistics without a model; wrapper methods train a model to evaluate feature subsets",
      "Filter methods are always more accurate than wrapper methods",
      "Wrapper methods can only select one feature at a time"
    ],
    "correct": 1,
    "explanation": "Filter methods (variance threshold, mutual information, correlation) score features with statistical measures. Wrapper methods (RFE, forward selection) train models repeatedly to evaluate different feature subsets."
  },
  {
    "id": "featsel-post-1",
    "stage": "post",
    "question": "Mutual information can detect relationships that Pearson correlation cannot. What kind?",
    "options": [
      "Linear relationships between continuous features",
      "Nonlinear relationships such as quadratic or periodic dependencies",
      "Relationships between categorical features only",
      "Relationships that require more than 1000 data points"
    ],
    "correct": 1,
    "explanation": "Pearson correlation only measures linear association. A quadratic relationship (y = x^2) has zero correlation but high mutual information. MI captures any statistical dependency between variables."
  },
  {
    "id": "featsel-post-2",
    "stage": "post",
    "question": "L1 (Lasso) regularization performs feature selection as part of training. How?",
    "options": [
      "It removes features with low variance before training starts",
      "It drives the weights of irrelevant features to exactly zero, effectively eliminating them from the model",
      "It ranks features by correlation with the target",
      "It trains separate models for each feature"
    ],
    "correct": 1,
    "explanation": "L1 regularization adds |w| penalty to the loss. The geometry of the L1 constraint (diamond shape) causes some weight solutions to land exactly at zero, producing sparse models that automatically select features."
  },
  {
    "id": "featsel-post-3",
    "stage": "post",
    "question": "RFE removes the least important feature and retrains. Why is this better than just removing all low-importance features at once?",
    "options": [
      "It is not better -- removing all at once is always preferred",
      "Feature importances change as features are removed, so iterative removal accounts for interactions between features",
      "RFE uses a different importance metric than single-step removal",
      "Removing one at a time is only necessary for neural networks"
    ],
    "correct": 1,
    "explanation": "Feature importances are relative. When a correlated feature is removed, the importance of its counterpart may increase. Iterative removal lets the model reassess importances at each step, capturing these interactions."
  }
]