gs gs126 Ensemble Learning - Boosting Weak Learnability - Quiz No.1
gs gs126 Neural Networks Quiz
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Question 2: Which of the following statements is not true about boosting?
It uses the mechanism of increasing the weights of misclassified data in preceding classifiers
It mainly increases the bias and the variance
It tries to generate complementary base-learners by training the next learner on the mistakes of the previous learners
It is a technique for solving two-class classification problems
Question 5: Which of the following statements is not true about weak learners?
They can be used as the building blocks for designing more complex models by combining them
Boosting learns the weak learners sequentially in a very adaptive way
They are combined using a deterministic strategy
They have low bias
Question 6: Which of the following is not related to boosting?
Non uniform distribution
Re-weighting
Re-sampling
Sequential style
Question 9: Which of the following is not true about boosting?
It considers the weightage of the higher accuracy sample and lower accuracy sample
It helps when we are dealing with bias or under-fitting in the data set
Net error is evaluated in each learning step
It always considers the overfitting or variance issues in the data set
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