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In a scenario where you have limited labeled data for training, how can ensemble techniques help improve the performance of your machine learning model?
A
Use data augmentation techniques to generate synthetic data and train multiple models on the augmented dataset.
B
Use transfer learning to leverage pre-trained models and fine-tune them on the limited labeled data.
C
Use ensemble techniques like bagging to train multiple models on different subsets of the limited labeled data, reducing the risk of overfitting.
D
All of the above
