Stacking Ensemble - RF to Logistic Regression
About This Architecture
Stacking ensemble pipeline combining five Random Forest base models with Logistic Regression as a meta-learner for improved classification accuracy. Raw data from the 5 Countries Dataset flows through ingestion, missing value imputation, normalization, feature engineering, and categorical encoding to produce a clean dataset. Each of the five Random Forest models generates probability predictions that feed into an aggregator, which passes meta-features to the Logistic Regression meta-model for final predictions. This two-level stacking approach reduces overfitting and leverages the strengths of both tree-based and linear classifiers. Fork and customize this diagram on Diagrams.so to adapt ensemble architectures for your datasets and model combinations.
People also ask
How do you build a stacking ensemble that combines Random Forest models with Logistic Regression as a meta-learner?
This diagram shows a complete stacking pipeline where five Random Forest models train on preprocessed data and generate probability predictions. These predictions feed into a Logistic Regression meta-model that learns optimal feature combinations, producing final predictions with reduced overfitting and improved generalization.
- Domain:
- Ml Pipeline
- Audience:
- Data scientists and ML engineers implementing ensemble learning methods
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