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DA0-002 Data Analysis Practice Question

A data analyst wants to predict customer churn based on categorical features like region and plan type, and continuous features like usage and tenure. Which regression type should be used?

⚠ Common exam trap

CompTIA often tests the misconception that 'regression' in the option name implies it is only for continuous outcomes, leading candidates to overlook logistic regression as a valid classification technique.

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

Logistic regression

Logistic regression is the correct choice because the target variable, customer churn, is binary (churn vs. no churn). Logistic regression models the probability of a binary outcome using a sigmoid function, making it suitable for classification tasks with both categorical and continuous predictors.

Answer analysis

Option-by-option breakdown

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

  • Logistic regression

    Why this is correct

    Logistic regression is used for binary classification, suitable for churn prediction.

  • Ridge regression

    Why it's wrong here

    Ridge regression is a regularized linear regression, still for continuous outcomes.

  • Linear regression

    Why it's wrong here

    Linear regression predicts a continuous outcome, not binary.

  • Lasso regression

    Why it's wrong here

    Lasso regression is also for continuous outcomes with feature selection.

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