Mean Imputation Method
What is Mean Imputation?
Mean Imputation is a simple method for handling missing values where each missing value is replaced with the mean (average) of the non-missing values for that feature.
How It Works
- Calculate the mean of observed values for each feature
- Replace all missing values in that feature with the calculated mean
Advantages
- Simple: Easy to understand and implement
- Fast: Computationally efficient
- Preserves Mean: Does not change the feature's mean
Disadvantages
- **Reduces Variance: Artificially reduces variability in data
- Ignores Correlations: Doesn't account for relationships between features
- Biased Estimates: Can introduce bias in statistical analyses
When to Use It
- Small amount of missing data: When < 5% of data is missing
- Exploratory analysis: Quick initial analysis
- Baseline comparison: Compare with more sophisticated methods
Related Methods
- Iterative Imputer: More sophisticated method
- Median Imputation: More robust to outliers
Related Terms
- Missing Values: Data gaps in the dataset