Among all the given statements, the ones that are true in the context of ensemble learning are:
Ensembles are more complex than base models, but they are not sensitive to slight variations in the data, hence robust.
Ensembles are machine learning methods for combining predictions from multiple separate models.
Hence, Option B is correct.
<h3>What is ensemble learning?</h3>
There is a process which is named "ensemble learning. It is a process that helps in learning about multiple models. These models include classification and exports, which are strategically generated and combined.
The basic reason behind using ensemble learning is to solve a particular intelligence problem. With the help of ensemble learning, one can primarily improve their classification, prediction, function, and many more qualifications.
Thus, Option B is correct
Learn more about ensemble learning from here:
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The complete question is attached in text form:
Which of the following statement(s) is/are true for ensemble learning?
A) Individual base learners in an ensemble model need to be dependent on each other in order to get a better prediction.
B) Ensembles are more complex than base models but they are not sensitive to slight variations in the data, hence, robust.
C) Ensembles are machine learning methods for combining predictions from multiple separate models.
D) The ensemble models are only used in a classification problem.
O A and B
O B and C
O C and D
O A and D
