Machine Learning MCQ - What does AdaBoost do before determining the next weak classifier?

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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 increases the weights of the training examples that were misclassified by the previous weak classifier before determining the next weak classifier 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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What is ensemble learning?

AdaBoost is a linear classifier

What is boosting?

How does AdaBoost learns?

Which is the best boosting algorithm?

Boosting helps to decrease the bias of a model

bagging works in parallel whereas boosting works in sequential manner

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Machine Learning MCQ - Differences between bagging and boosting

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, bagging, boosting, differences between bagging and boosting, bagging vs boosting

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

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1. Which among the following are some of the differences between bagging and boosting?

a) In bagging we use the same classification algorithm for training on each sample of the data, whereas in boosting, we use different classification algorithms on the different training data samples

b) Bagging is easy to parallelize whereas boosting is inherently a sequential process

c) In bagging we typically use sampling with replacement whereas in boosting, we typically use weighted sampling techniques

d) In comparison with the performance of a base classifier on a particular data set, bagging will generally not increase the error whereas as boosting may lead to an increase in the error

 

Answer: (b), (c), and (d)   (b) Bagging (Bootstrap Aggregation) is an ensemble learning method which trains multiple models independently in parallel. Boosting is an ensemble learning method which trains each new model such that it focuses on correcting the errors made by the previous model. (c) In the case of Bagging, any element has the same probability to appear in a new data set. Training data subsets are drawn randomly with a replacement for the training dataset. However, for Boosting, the observations are weighted. In Boosting algorithms each classifier is trained on data, taking into account the previous classifiers’ success. Hence, every new training subset comprises the elements that were misclassified by previous models. Misclassified data increases its weights to emphasize the most difficult cases. (d) Boosting can result in an increase in error over a base classifier due to over-emphasis on existing noise data points in later iterations.   Other differences between bagging and boosting Difference Bagging Boosting Base classifiers training They are trained in parallel They are trained in sequential manner. Bias and variance Decreases model’s variance Decreases model’s bias Overfitting problem Solves the problem Increases the problem Weights of the model Models receive equal weights Models are weighed according to their performance. Model building Each model built independently Models are influenced by the performance of the previous models. When to apply If the classifier shows high variance (unstable). If the classifier shows high bias (stable).

 

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• Data Warehousing and Data Mining MCQ with answers home
  

What is ensemble learning?

Difference between bagging and boosting ensemble learning techniques in ML

What is bagging?

What is boosting?

Boosting vs bagging

When to use boosting and when to use bagging?

Which is best - bagging or boosting? 

bagging helps in decreasing variance of a model, boosting helps to decrease the bias of a model

bagging works in parallel whereas boosting works in sequential manner

Machine learning solved mcq, machine learning solved mcq