Which scenario BEST illustrates concept drift?

The correct answer is B. Changing user behavior over time. Concept drift occurs when the statistical relationship between input features and the target variable changes over time, making a previously well-trained model progressively less accurate. User behavior shifting (e.g., purchasing patterns changing due to economic conditions or tr

NVIDIA Certified Associate (NCA) Core AI Concepts

Question

Options

ARandom measurement noise
BChanging user behavior over time
CIncorrect labels
DFeature normalization errors

How the community answered

(56 responses)

A
2% (1)
B
88% (49)
C
7% (4)
D
4% (2)

Explanation

Concept drift occurs when the statistical relationship between input features and the target variable changes over time, making a previously well-trained model progressively less accurate. User behavior shifting (e.g., purchasing patterns changing due to economic conditions or trends) is the classic real-world example: the model learned associations from past behavior that no longer hold. Random measurement noise (A) is stationary and does not shift the underlying distribution. Incorrect labels (C) are a data quality problem, not a temporal distribution shift. Feature normalization errors (D) are a preprocessing mistake, not a change in the data-generating process.

Topics

#Concept Drift#Machine Learning Concepts#Model Monitoring

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