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MLA-C01 Concept drift Practice Question

A machine learning engineer is troubleshooting a model that is producing unexpectedly low accuracy in production. The engineer examines the model's training data and finds that the distribution of the target variable in production is significantly different from the training set. What type of drift is the model experiencing?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Concept drift

Concept drift refers to any change in the statistical relationship between input features and the target variable, including changes in the target variable distribution. The scenario describes a change in the target variable distribution, which is a form of concept drift, specifically prior probability shift. Option A (Prior probability shift) is indeed a subtype of concept drift, but it is more specific; the question asks for the general drift type, making concept drift the best answer. Option C (Data drift) refers to changes in the distribution of input features, not the target. Option D (Covariate shift) is a form of data drift where the input distribution changes while the conditional distribution P(Y|X) remains unchanged.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Prior probability shift

    Why it's wrong here

    Prior probability shift is a specific case of concept drift where class proportions change.

  • Concept drift

    Why this is correct

    Concept drift is a change in the statistical properties of the target variable.

  • Data drift

    Why it's wrong here

    Data drift is a broad term; concept drift is more specific.

  • Covariate shift

    Why it's wrong here

    Covariate shift refers to changes in input features, not the target.

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