Question

In ensemble learning, you aggregate the predictions for weak learners, so that an ensemble of these models will give a better prediction than prediction of individual models. Which of the following statements is / are true for weak learners used in ensemble model?
1. They don’t usually overfit.
2. They have high bias, so they cannot solve complex learning problems
3. They usually overfit.In ensemble learning, you aggregate the predictions for weak learners, so that an ensemble of these models will give a better prediction than prediction of individual models. Which of the following statements is / are true for weak learners used in ensemble model?
1. They don’t usually overfit.
2. They have high bias, so they cannot solve complex learning problems
3. They usually overfit.

Answer 1

option 1 and 2 both are correct

Answer 2

Answer:

The statement (1) and (2) are true for weak learners used in the ensemble model.

Explanation:

• Ensemble learning is the operation by which numerous illustrations, such as classifiers or professionals, are strategically developed and integrated to solve a certain computational intellect problem.
• The three primary types of ensemble learning techniques are bagging, stacking, and boosting.
• The correct options are:

(1) They don’t usually overfit.

(2) They have high bias, so they cannot solve complex learning problems.

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