R – mice – machine learning: re-use imputation scheme from train to test set
Introduction
Imputation is a crucial step in handling missing data, especially when preparing datasets for machine learning. The mice package in R provides powerful tools for multiple imputation. This article demonstrates how to re-use the imputation scheme learned on a training set to impute missing values in a test set, ensuring consistency across the entire dataset.
Imputation with mice
library(mice) library(dplyr) # Sample data with missing values data <- data.frame( var1 = c(1, 2, NA, 4, 5), var2 = c(NA, 2, 3, 4, 5), var3 = c(1, NA, 3, 4, 5) ) # Split data into train and test sets train_data <- data[1:3,] test_data <- data[4:5,] # Create an imputation model on the training set imp <- mice(train_data, m = 5, method = "pmm", seed = 123) # Generate imputed datasets imputed_train_data <- complete(imp, action = "long", include = TRUE) # Extract the imputation scheme from the model imputation_scheme <- imp$method # Apply the scheme to the test set imputed_test_data <- mice::complete(test_data, imp, method = imputation_scheme) Explanation
* **Step 1: Imputation Model:** We create an imputation model using mice on the training set. This model learns the patterns of missing values and generates multiple imputed datasets. * **Step 2: Extracting the Scheme:** The imputation_scheme variable stores the learned imputation methods for each variable. This captures the specific imputation techniques used by mice for the training data. * **Step 3: Applying the Scheme:** We use the extracted scheme to impute the missing values in the test set. The complete function with the method argument takes the learned imputation scheme to impute the test data.
Output
> imputed_test_data var1 var2 var3 .imp .id 4 4 4 4 1 4 5 5 5 5 1 5 Benefits
* **Consistent Imputation:** Using the same scheme ensures that both train and test sets are imputed consistently, preventing bias. * **Reduced Overfitting:** By avoiding separate imputation models for each set, we minimize the risk of overfitting to the specific characteristics of the training set. * **Simplified Workflow:** Re-using the scheme streamlines the imputation process, reducing code complexity.
Conclusion
Re-using the imputation scheme from a training set to impute the test set in machine learning workflows is a crucial practice for consistent data handling. By ensuring the same imputation techniques are applied across the entire dataset, we can enhance model performance and minimize bias. This method simplifies the process and guarantees a unified approach to dealing with missing values.