MLS-C01 Exploratory Data Analysis Practice Question
Which TWO actions are appropriate when handling missing data in a dataset for machine learning? (Select TWO.)
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
✓
Use a machine learning model to predict missing values based on other features
Options A and C are correct. Using a machine learning model to predict missing values is a valid imputation technique that can preserve relationships in the data. Imputing with the mean or median is a standard approach for numerical features and maintains the dataset size. Option B is incorrect because dropping all rows with any missing values can lead to significant data loss, especially if missingness is widespread. Option D is incorrect because removing an entire feature due to missing values might discard predictive information unless the feature is mostly missing. Option E is incorrect because filling all missing values with zero can introduce bias and distort distributions, as zero may not be a natural placeholder for the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a machine learning model to predict missing values based on other features
Why this is correct
Correct. Using a model to predict missing values based on other features is a sophisticated imputation method that leverages correlations in the data.
- ✗
Drop all rows that contain any missing value
Why it's wrong here
Incorrect. Dropping all rows with missing values can discard a large portion of the dataset, potentially leading to loss of valuable information and biased models.
- ✓
Impute missing values with the mean or median of the feature
Why this is correct
Correct. Imputing with the mean (for normally distributed data) or median (for skewed data) is a common and simple technique that preserves the central tendency of the feature.
- ✗
Remove the feature entirely if it contains missing values
Why it's wrong here
Incorrect. Removing a feature entirely because it has missing values could eliminate important predictive information; imputation or modeling is generally preferred.
- ✗
Fill missing values with zero
Why it's wrong here
Incorrect. Filling missing values with zero is often inappropriate because zero may not be a meaningful value for the feature and can distort statistical properties and model performance.
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