blending

**Blending (Ensemble Method)** **Overview** Blending is an ensemble machine learning technique that uses a held-out validation set to train a meta-learner. It is often considered a simpler, "leakage-free" variation of Stacking. **The Process** 1. **Split**: Divide the training data into two disjoint sets: Train (70%) and Holdout (30%). 2. **Level 1**: Train base models (e.g., XGBoost, Neural Net) on the 70% Train set. 3. **Predict**: Use these models to make predictions on the 30% Holdout set. 4. **Level 2**: Create a new dataset where the features are the specific predictions from Level 1, and the target is the real target. 5. **Meta-Learn**: Train a final model (e.g., Linear Regression) on this new dataset. **Pros & Cons** - **Pros**: Prevents information leakage because the meta-learner never sees the data used to train the base models. Extremely robust against overfitting. - **Cons**: Less data efficient. You sacrifice 30% of your training data just to train the meta-learner, whereas Stacking uses 100% via Cross-Validation.