MLS-C01 Modeling Practice Question
A machine learning team is using Amazon SageMaker to build a regression model. The target variable is heavily right-skewed with a long tail. Which data transformation should the team apply to the target variable before training?
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
A log transformation compresses the range of the target and makes the distribution more symmetric, improving model performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables, not target transformation.
- ✗
Min-max scaling
Why it's wrong here
Scaling does not change skewness.
- ✓
Log transformation
Why this is correct
Log transform reduces right skew and makes distribution more normal.
- ✗
Standardization (z-score)
Why it's wrong here
Standardization centers data but does not reduce skew.
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