The correct answer is B. FALSE.
Ensembles are a type of machine learning model that combines the predictions of multiple models to produce a more accurate prediction. When there is significant diversity among the models, the ensemble can be more accurate than any of the individual models. This is because the different models will make different mistakes, and the ensemble can learn from these mistakes to produce a more accurate prediction.
For example, let’s say we have three models that are predicting the price of a house. Model A predicts that the house will sell for $100,000, model B predicts that it will sell for $150,000, and model C predicts that it will sell for $200,000. If we average these predictions, we get a prediction of $150,000. This is more accurate than any of the individual predictions, because it takes into account the different biases of the models.
Of course, there are some cases where ensembles can yield bad results. For example, if the models are all very similar, then the ensemble will not be able to learn from their mistakes and will not be more accurate than any of the individual models. Additionally, if the models are all very noisy, then the ensemble will be more noisy than any of the individual models.
Overall, however, ensembles are a powerful tool that can be used to improve the accuracy of machine learning models. When there is significant diversity among the models, the ensemble can be more accurate than any of the individual models.