Iterative Imputer Method

What is Iterative Imputer?

Iterative Imputer (also called MICE - Multivariate Imputation by Chained Equations) is a sophisticated method for handling missing values that models each feature with missing values as a function of other features.

How It Works

1. Initial Imputation: Simple imputation (mean, median, etc.)
2. Iterative Process:
   • For each feature with missing values:
     • Treat that feature as the target (y)
     • Use other features as predictors (X)
     • Train a model on observed values
     • Predict missing values
3. Repeat: Continue until convergence

Advantages

• Preserves Relationships: Captures correlations between features
• More Accurate: Usually better than simple imputation
• Flexible: Can use various regression models

When to Use It

• Missing Not at Random: When missingness depends on other features
• Correlated Features: When features are correlated
• Moderate Missingness: When missing data is not excessive

Related Methods

• Mean Imputation: Simple but less accurate
• KNN Imputation: Uses similar samples

Related Terms

• Missing Values: Data gaps in the dataset