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Computer Application- ICAR JRF/SRF 2024
MCQ (Single Correct)
Choose the one which is not an Ensemble Modeling Technique
- 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
Explanation not matching this question?
क्या व्याख्या इस प्रश्न से मेल नहीं खा रही?
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