The correct answer is: A. less correlation among predictions.
An ensemble method is a technique in machine learning that combines the predictions of multiple models to produce a more accurate prediction than any of the individual models could produce on its own. Ensemble methods work by reducing the variance of the predictions of the individual models. This is done by averaging the predictions of the individual models, or by using a voting scheme to select the most likely prediction from the individual models.
The correlation between the predictions of the individual models is an important factor in the performance of an ensemble method. If the predictions of the individual models are highly correlated, then the ensemble method will not be able to reduce the variance of the predictions. This is because the ensemble method will simply average the predictions of the individual models, which will result in a prediction that is highly correlated with the predictions of the individual models.
On the other hand, if the predictions of the individual models are not correlated, then the ensemble method will be able to reduce the variance of the predictions. This is because the ensemble method will average the predictions of the individual models, which will result in a prediction that is not correlated with the predictions of the individual models.
Therefore, an ensemble method works better, if the individual base models have less correlation among predictions.
Here is a brief explanation of each option:
- Option A: Less correlation among predictions. This is the correct answer. As explained above, an ensemble method works better, if the individual base models have less correlation among predictions.
- Option B: High correlation among predictions. This is the wrong answer. As explained above, an ensemble method works better, if the individual base models have less correlation among predictions.
- Option C: Correlation does not have any impact on ensemble output. This is the wrong answer. As explained above, the correlation between the predictions of the individual models is an important factor in the performance of an ensemble method.
- Option D: None of the above. This is the wrong answer. As explained above, the correct answer is Option A.