MLS-C01 Exploratory Data Analysis Practice Question
Which THREE of the following are best practices for feature engineering during EDA? (Select THREE.)
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that all preprocessing steps, like outlier removal and standardization, should be performed during EDA, when in fact EDA is for understanding data distributions and relationships, while transformations and scaling are part of data preprocessing that may follow EDA based on insights gained.
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 highly skewed features
Applying a log transformation to highly skewed features helps normalize their distribution, reducing the impact of extreme values and making the data more suitable for many machine learning algorithms that assume normally distributed features. This is a common technique during exploratory data analysis (EDA) to stabilize variance and improve model performance, especially for linear models and neural networks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all outliers from the dataset
Why it's wrong here
Removing all outliers may lose important information.
- ✗
Standardize all features to have zero mean and unit variance
Why it's wrong here
Standardization is not always necessary and depends on the model.
- ✓
Apply log transformation to highly skewed features
Why this is correct
Log transformation reduces skewness.
- ✓
Create interaction features between numeric variables
Why this is correct
Interaction features capture relationships between variables.
- ✓
Encode categorical variables using one-hot encoding
Why this is correct
One-hot encoding is common for categorical variables.
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