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I have read that in ensemble learning we use the outputs of various classifiers to make the predictive modeling better but in Adaboost we just use one classifier and we make it a strong learner but how it is a part of ensemble learning.

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AdaBoost or Adaptive Boost is a boosting ensemble model which works by learning from it's previous mistakes, ie: misclassified data points.

We specify the number of decision trees to be generated while training and during each training step, it calculates the following :

  1. The weighted error rate of the trained decision tree
  2. The decision tree's weight in the ensemble = learning rate * log((1-e)/e)
  3. Update weights of wrongly classified points

This process repeats until all the trees are trained. In the end, AdaBoost makes all of them "vote" by adding up the weight (of each tree) multiplying by the prediction (of each tree).

Therefore, this becomes an ensemble of multiple decision trees.

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  • $\begingroup$ The information is rather nice but I think you missed partially the op question. You can improve by emphasizing that there are multiple weak learners, even if all of them are trees. I give you a point to encourage you $\endgroup$ – rapaio Jul 20 '19 at 17:55
  • $\begingroup$ Oh alright. Thank you for the pointer, much appreciated. $\endgroup$ – Raghav Kukreti Jul 20 '19 at 18:07
  • $\begingroup$ So,in Adaboost we cann't use different classifiers together right like Naivebayes classifier,SVM,DecisionTree etc. $\endgroup$ – raghav gaur Jul 21 '19 at 6:33

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