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Model Evaluation and Validation Flashcards

7 cards from real Data Science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. In a binary classifier, what does precision measure?

    Answer: Of all predicted positives, how many were actually positive

    Precision = TP / (TP + FP), the proportion of positive predictions that are correct.

  2. What does recall (sensitivity) measure?

    Answer: Of all actual positives, how many were correctly identified

    Recall = TP / (TP + FN), the proportion of actual positives correctly detected.

  3. Why is accuracy a poor metric for highly imbalanced datasets?

    Answer: A model predicting the majority class always can score high while missing the minority class

    With rare positives, always predicting the majority class yields high accuracy despite no useful detection.

  4. The F1 score is best described as the:

    Answer: Harmonic mean of precision and recall

    F1 = 2·(precision·recall)/(precision+recall), the harmonic mean balancing both.

  5. What does the ROC curve plot?

    Answer: True positive rate against false positive rate across thresholds

    The ROC curve plots TPR vs FPR as the classification threshold varies.

  6. An AUC of 0.5 indicates a classifier that:

    Answer: Performs no better than random guessing

    AUC = 0.5 corresponds to chance-level discrimination between classes.

  7. In a confusion matrix, a false negative occurs when the model:

    Answer: Predicts negative for an actual positive case

    A false negative is an actual positive that the model labeled negative.