__________ refers to the scenario in which the analyst builds a model that does a great job of explaining the sample of data on which it is based but fails to accurately predict outside the sample data.
A)Underfitting
B)Overfitting
C)Oversampling
D)Undersampling

Question

Quizplus · Accepted Answer

Overfitting refers to when a model becomes too complex, to the point where it is perfectly fit to the sample data, but performs poorly on new or unseen data. This is because the model has essentially "memorized" the sample data and is unable to generalize to new situations. Underfitting, on the other hand, occurs when a model is too simple and fails to capture the underlying patterns in the data, leading to poor performance on both the sample data and new data. Oversampling and undersampling refer to data preprocessing techniques used to balance imbalanced datasets, and are not related to overfitting or underfitting.

__________ Refers to the Scenario in Which the Analyst Builds