Question at position 13 Multiple Choice Question: A fitting curve plots Generalization performance vs. model complexityTrue positive rate vs. false negative rateTrue positive rate vs. false positive rateGeneralization performance vs. size of training set

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BlackTom AI · Accepted Answer

When evaluating what a fitting curve typically shows in machine learning concepts, we should consider what is most directly related to how models fit data.
Option 1: Generalization performance vs. model complexity. This aligns with the classic bias-variance tradeoff idea, where increasing model complexity can improve fit to training data up to a point but may harm generalization performance on unseen data. It is the standard way to visualize how well a model generalizes as we vary complexity.
Option 2: True positive rate vs. false negative rate. This pair represents a specific performance trade-off related to classification thresholds but is not typically described as a fitting curve; it’s more about how the classifier behaves as you move the decision boundary.
Option 3: True positive rate vs. false positive rate. This is the ROC curve, which plots TPR against FPR to assess discrimination ability across thresholds. While informative about performance, it is not a fitting curve showing generalization with respect to model fitting.
Option 4: Generalization performance vs. size of training set. This describes a learning curve, which shows how performance improves as more training data are provided, not how a model fits as complexity changes. While related to generalization, it’s conceptually distinct from a fitting curve that varies complexity.
Thus, the statement best describing a fitting curve is the one plotting generalization performance against model complexity, since it directly reflects how model suitability changes with added capacity to fit the data.

Question at position 13 Multiple Choice Question: A fitting curve plots Generalization performance vs. model complexityTrue positive rate vs. false negative rateTrue positive rate vs. false positive rateGeneralization performance vs. size of training set

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