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MLS-C01 Modeling Practice Question

A data scientist is building a regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, and location. After training a linear regression model, the scientist notices that the residuals have a pattern: they increase as the predicted value increases. Which action is most appropriate?

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

Apply a log transformation to the target variable

Patterned residuals (heteroscedasticity) violating linear regression assumptions. Log-transforming the target variable can stabilize variance. Adding polynomial features or interactions may help with non-linearity but not specifically for heteroscedasticity. Ridge regression is for multicollinearity, not for patterned residuals.

Answer analysis

Option-by-option breakdown

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

  • Remove outliers from the dataset

    Why it's wrong here

    Outliers may cause patterns but not necessarily the increasing variance pattern described.

  • Use Ridge regression instead of linear regression

    Why it's wrong here

    Ridge regression addresses multicollinearity, not patterned residuals.

  • Add polynomial features to the model

    Why it's wrong here

    Polynomial features address non-linearity, not heteroscedasticity.

  • Apply a log transformation to the target variable

    Why this is correct

    Log transformation can stabilize variance and reduce heteroscedasticity.

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