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# Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, ensemble learning, boosting, AdaBoost algorithm, how does AdaBoost make use of weak learners, how does boosting algorithm work?

## Machine Learning MCQ - Differences between ensemble learning methods - bagging and boosting

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1. The AdaBoost algorithm creates an ensemble of weak classifiers. Before determining the next weak classifier, which one of the following is done by the AdaBoost algorithm?

a) Chooses a new random subset of the training examples to use

b) Decreases the weights of the training examples that were misclassified by the previous weak classifier

### c) Increases the weights of the training examples that were misclassified by the previous weak classifier

d) Removes the training examples that were classified correctly by the previous weak classifier

Answer: (c) Increases the weights of the training examples that were misclassified by the previous weak classifier

Boosting

### Boosting is an ensemble learning method which trains each new model such that it focuses on correcting the errors made by the previous model.

Boosting uses homogeneous weak learners in sequential manner to learn and tries to reduce bias on final predictions.

Boosting ensemble modeling works on the following principle. First, a model is built from the training data. Then the second model is built which tries to correct the errors present in the first model. This procedure is continued and models are added until either the complete training data set is predicted correctly or the maximum number of models have been added.

### AdaBoost or Adaptive Boosting is one of the ensemble boosting classifier. It combines multiple weak classifiers to increase the accuracy of classifiers.

AdaBoost fits a sequence of weak learners on different weighted training data. It starts by predicting the original data set and gives equal weight to each observation. If prediction is incorrect using the first learner, then it gives higher weight to observation which have been predicted incorrectly. Being an iterative process, it continues to add learner(s) until a limit is reached in the number of models or accuracy.

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### AdaBoost is a linear classifier

What is boosting?