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Answer: Use ensemble techniques like stacking to combine the predictions of multiple models, each trained on a different method for handling outliers, such as robust scaling or transformation.
In a dataset with outliers, ensemble techniques like stacking can help improve the performance of the machine learning model. Stacking involves combining the predictions of multiple models, each trained on a different method for handling outliers, such as robust scaling or transformation. This allows the ensemble to capture the different ways of dealing with outliers and improve the model's robustness. Bagging and boosting can also be used, but they do not specifically address the issue of outliers. Therefore, the correct answer is C, as it directly addresses the use of ensemble techniques in this scenario.
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In a scenario where you have a dataset with outliers, 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 removing or adjusting the outliers.
B
Use ensemble techniques like boosting to train models sequentially, where each model focuses on the instances with outliers and tries to predict their values more accurately.
C
Use ensemble techniques like stacking to combine the predictions of multiple models, each trained on a different method for handling outliers, such as robust scaling or transformation.
D
All of the above
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