MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is preparing data for a regression model. The target variable has a skewed distribution. The scientist wants to apply a log transformation to make it closer to normal. Which step should be taken before applying log transformation?
⚠ Common exam trap
AWS often tests the assumption that candidates will confuse data normalization or centering with the domain restriction of the log function, leading them to pick standardization or mean-centering as a preparatory step.
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
✓
Ensure all values are positive
The log transformation is defined only for positive real numbers; applying it to zero or negative values results in undefined or complex outputs. Therefore, before applying a log transformation, you must ensure all values in the target variable are positive, typically by adding a constant (e.g., log(x + 1)) if zeros are present. This step is a fundamental data preparation requirement for log transformations in regression modeling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Standardize the data to zero mean and unit variance
Why it's wrong here
Standardization can also result in negative values.
- ✗
Remove outliers using IQR
Why it's wrong here
Outlier removal is unrelated to the prerequisite of positive values for log.
- ✓
Ensure all values are positive
Why this is correct
Log is undefined for zero and negative values. If present, add a constant or use other transformations.
- ✗
Center the data by subtracting the mean
Why it's wrong here
Centering can introduce negative values, making log transformation invalid.
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