MLS-C01 Exploratory Data Analysis Practice Question
A machine learning team is reviewing a dataset for a regression problem. They notice that the target variable has a right-skewed distribution. Which transformation should they consider applying 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
✓
Apply log transformation to the target variable.
Log transformation is commonly applied to right-skewed data to make it more normally distributed, which can improve model performance. Option A (StandardScaler) is for scaling, not skewness. Option B (MinMaxScaler) also doesn't address skewness. Option D (One-hot encoding) is for categorical variables.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply StandardScaler to the target variable.
Why it's wrong here
StandardScaler standardizes but does not reduce skewness.
- ✗
Apply MinMaxScaler to the target variable.
Why it's wrong here
MinMaxScaler scales to a range but does not address skewness.
- ✓
Apply log transformation to the target variable.
Why this is correct
Log transformation reduces right skewness.
- ✗
Apply one-hot encoding to the target variable.
Why it's wrong here
One-hot encoding is for categorical variables, not continuous targets.
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