MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a linear regression model. The target variable is right-skewed. Which transformation should the data scientist apply to the target variable to improve model performance?
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 correct because applying a logarithmic transformation to a right-skewed target variable can reduce skewness and make the distribution more normal, which improves the performance of linear regression models. Option A (Min-max scaling) scales the data to a fixed range but does not address skewness. Option B (One-hot encoding) is used for categorical variables, not for transforming continuous targets. Option D (PCA) is for dimensionality reduction, not for correcting skewness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Min-max scaling
Why it's wrong here
Scaling does not reduce skewness.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables.
- ✓
Log transformation
Why this is correct
Log transformation reduces right skewness.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is for dimensionality reduction.
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