• Ensemble Modeling Techniques:
• Bagging (Bootstrap Aggregation): Trains multiple models independently on bootstrapped subsamples in parallel (e.g., Random Forest).
• Boosting: Trains models sequentially, where each successor model focuses on correcting residual errors of predecessor models (e.g., AdaBoost, XGBoost).
• Stacking (Stacked Generalization): Trains diverse base learners and uses a meta-model trained on cross-validated predictions to combine their outputs.
• Blending: A simplified heuristic variation of stacking that uses a single hold-out validation set rather than $k$-fold cross-validation. In standard academic machine learning curricula and competitive examinations, Blending is not classified as one of the fundamental theoretical ensemble architectures.
• Conclusion: Blending (Option 3).

Correct option: (3) Blending