AI0-001 AI Models and Data Engineering Practice Question
A team is building a regression model to predict house prices. Which data transformation is most appropriate if the target variable exhibits right skewness?
⚠ Common exam trap
CompTIA often tests the misconception that standardization can fix skewness, but candidates must remember that standardization only rescales the data, not reshape its distribution.
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
✓
Log transformation
Log transformation is the most appropriate technique for right-skewed target variables because it compresses the long tail, making the distribution more symmetric and closer to Gaussian. This stabilizes variance and often improves the performance of regression models that assume normally distributed errors, such as linear regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA reduces dimensionality by creating uncorrelated components; it does not correct skewness in the target. A log or square-root transform addresses right skew directly. PCA is tempting because it is a common preprocessing step, but it is intended for feature reduction and multicollinearity, not distribution shaping of the label.
- ✗
Standardization (Z-score)
Why it's wrong here
Standardization rescales features to zero mean and unit variance; it preserves the target's right skew, so the regression assumption remains violated. It is tempting because it is the standard preprocessing step for distance-based or regularised models, where it would correctly be applied to predictor variables.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding converts categorical variables into binary columns and cannot reduce right skewness in a continuous target. It is tempting because it is the standard transformation for nominal features, which is the correct scenario for encoding categorical predictors in a regression model.
- ✓
Log transformation
Why this is correct
Log transformation compresses the long right tail of a positively skewed target, pulling extreme values toward the mean and stabilising variance. This satisfies the right-skewness constraint, making the target closer to normal so linear regression's residual assumptions hold and predictions are less distorted.
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