The correct answer is B. No.
Ensembling is a technique in machine learning that combines the predictions of multiple models to improve the overall accuracy. This can be done by averaging the predictions of the models, or by using a voting scheme to select the most likely prediction.
Ensembling can be effective in reducing the variance of the predictions, which can improve the accuracy of the model. However, it is not necessary to tune the hyperparameters of all base models to improve the ensemble performance. In fact, doing so can sometimes lead to worse results.
The reason for this is that the hyperparameters of the base models are often correlated with each other. When you tune the hyperparameters of one model, you are also affecting the hyperparameters of the other models. This can lead to a situation where the models are no longer independent of each other, and the ensemble performance suffers.
Therefore, it is generally best to leave the hyperparameters of the base models alone when creating an ensemble. If you do want to tune the hyperparameters, it is best to do so for each model individually, and then use the best performing models in the ensemble.
Here are some additional details about each option:
- A. Yes. This option is incorrect because it is not necessary to tune the hyperparameters of all base models to improve the ensemble performance.
- B. No. This option is correct because it is not necessary to tune the hyperparameters of all base models to improve the ensemble performance.
I hope this helps! Let me know if you have any other questions.