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In a scenario where you have a dataset with noisy data, how can ensemble techniques help improve the performance of your machine learning model? Explain the process and the benefits of using ensemble techniques in this case.
A
Use ensemble techniques like bagging to train multiple models on different subsets of the dataset, where each subset is created by adding random noise to the original data.
B
Use ensemble techniques like boosting to train models sequentially, where each model focuses on the instances with higher noise and tries to correct their predictions.
C
Use ensemble techniques like stacking to combine the predictions of multiple models, each trained on a different level of noise reduction or smoothing.
D
All of the above
