For an imbalanced dataset, why can accuracy be considered a misleading metric?

The correct answer is B. It may simply reflect the class distribution. In imbalanced datasets, accuracy can appear very high simply because the model predicts the majority class for most examples, failing to capture performance on the minority class. For example, if 95% of cases are of one class, a model predicting only this class achieves 95%…

Modeling, Analysis, and Outcomes

Question

Options

AIt always underestimates model performance.
BIt may simply reflect the class distribution.
CIt overcomplicates the evaluation process.
DIt is computationally too demanding to calculate.

How the community answered

(47 responses)

A
4% (2)
B
83% (39)
C
2% (1)
D
11% (5)

Explanation

In imbalanced datasets, accuracy can appear very high simply because the model predicts the majority class for most examples, failing to capture performance on the minority class. For example, if 95% of cases are of one class, a model predicting only this class achieves 95% accuracy, despite being useless for identifying the minority class. This makes accuracy misleading, as it doesn't truly indicate how well the model differentiates between different classes in the data.

Topics

#imbalanced dataset#accuracy#evaluation metrics#class distribution

Community Discussion

No community discussion yet for this question.

Full DY0-001 Practice