The correct answer is B. FALSE. Ensemble learning can be applied to both supervised and unsupervised learning methods.
In supervised learning, the model is trained on a labeled dataset, which means that each data point has a corresponding label. The goal of supervised learning is to learn a function that can map new data points to their correct labels.
In unsupervised learning, the model is trained on an unlabeled dataset. The goal of unsupervised learning is to find patterns in the data without any labels.
Ensemble learning is a type of machine learning that combines the predictions of multiple models to improve the accuracy of the overall prediction. Ensemble learning can be applied to both supervised and unsupervised learning methods.
One common type of ensemble learning is bagging. Bagging is a technique that creates multiple models by sampling the training data with replacement. The predictions of the multiple models are then combined to produce a final prediction.
Another common type of ensemble learning is boosting. Boosting is a technique that creates multiple models by iteratively adding models to the ensemble. Each model is trained to correct the errors of the previous models.
Ensemble learning can be a very effective way to improve the accuracy of machine learning models. However, it is important to choose the right ensemble learning technique for the task at hand.