ML Python 200 - Impute Categorical and Numeric with Mice LightGBM

Two-Step MICE Imputation Workflow in KNIME with miceforest

This process trains a multiple imputation model on an initial dataset using miceforest (Step 1) and then applies the trained model to new data (Step 2). The training step prepares the schema, fits the LightGBM-based MICE kernel, and saves both the model and metadata. The application step ensures new data exactly matches the training schema before imputing missing values, guaranteeing consistent and reliable imputations across datasets.

External resources

• MEDIUM BLOG - Data preparation for Machine Learning with KNIME and the Python “vtreat” package
• Handling “Missing Data” Like a Pro — Part 3: Model-Based & Multiple Imputation Methods