![]() ![]() ![]() Their descriptions are all correct but incomplete. The blind men are each describing an elephant from their own point of view. For example, if we choose a classification tree, Bagging and Boosting would consist of a pool of trees as big as we want as shown in the following diagram: Consider the fable of the blind men and the elephant depicted in the image below. To use Bagging or Boosting you must select a base learner algorithm.Boosting helps to decrease the model’s bias. Bagging helps to decrease the model’s variance.So the result may be a model with higher stability and reliability. These two lead to the decrease of the variance as compared to that of a single estimate as they combine several estimates from different models.These weak learners are allowed to predict and clas- sify and based on the mean or majority the outcome is determined due to which we achieve diversification by having multiple models for each of the learners. ![]() Strong learner by combining these weak learners which performs better than a single strong learner. ![]()
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