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Replacing missing values with the mean of observed values in the feature.
DESCRIPTION
Mean imputation fills missing entries with the feature’s average, providing a simple, fast baseline for numeric data. It preserves the mean but reduces variance and can bias correlations. It is acceptable under MCAR but risky under MAR or MNAR. Use carefully, often only as a baseline or within robust evaluation. Modern practice favors model-based or multiple imputation for unbiased inference.
ALTERNATIVE DEFINITIONS
scikit-learn
Replace missing values using the mean along each column.
Specifies column-wise mean replacement within an imputation transformer.
Penn State Eberly College of Science
Mean imputation replaces missing values with the average of the observed values.
Highlights simplicity and typical biases introduced.
RELATED TERMS
KNN Imputation Listwise Deletion Missing Data MCAR MAR MNAR
COMPETING TERMS
Median Imputation Multiple Imputation
RELATIONS
- Broader: Imputation
KNOWLEDGE AREAS
Data Science AI Data Quality MLOps
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer Data Steward Analytics Engineer
EXTERNAL SOURCES
- Wikipedia - Imputation (statistics)
- scikit-learn - SimpleImputer API docs
- Penn State Eberly College of Science - Lesson on mean imputation
- UCLA Statistical Consulting - Overview of missing data methods
TOP QUESTIONS
When is Mean Imputation acceptable?
It’s acceptable under MCAR assumptions or as a baseline. For inference or MAR/MNAR, prefer model-based or multiple imputation.
Category: Requirements
What is a common mistake with Mean Imputation?
Applying it without considering variance reduction and biasing correlations can mislead models and statistical tests.
Category: Mistakes
How does Mean Imputation compare to Median Imputation?
Mean is more sensitive to outliers; median is robust. Choose based on distribution shape and outlier presence.
Category: Alternatives
What risks does Mean Imputation pose?
It underestimates variability and can distort relationships, especially when missingness is not completely at random.
Category: Risks
How to apply Mean Imputation in scikit-learn?
Use SimpleImputer(strategy=‘mean’) within a Pipeline, fitting on training data and applying consistently to validation and test sets.
Category: HowTo
Is Mean Imputation suitable for time series?
Typically no; prefer interpolation or model-based imputation that respects temporal structure.
Category: Opinion
What hot topic affects Mean Imputation today?
Multiple imputation and generative models provide better uncertainty handling than simple mean replacement.
Category: Topic
Does Mean Imputation preserve the mean?
Yes, by construction, but it reduces variance and can bias covariances and regression coefficients.
Category: Misunderstanding
How should Mean Imputation be evaluated?
Compare model performance and inference metrics with alternative methods under cross-validation and sensitivity analyses.
Category: Validation