1.1 Which of the following statements about bagging is false? a. Bagging combine

Question

1.1 Which of the following statements about bagging is false?

a. Bagging combines several models

b. Bagging generally reduces variance and increases model stability

c. Bagging often reduces overfitting

d. Bagging often reduces bias

1.2 Which of the following statements about boosting is false?

a. Boosting combines several weak models into a stronger model

b. After a weak learner is added to the model, training data are reweighted

c. Boosting often reduces bias

d. Boosting is not sensitive to noisy data

Explanation / Answer

Answer 11:

c) Bagging often reduces overfitting is FALSE

Bagging is an approach to diminish the variance of your prediction by creating extra data for training from your unique dataset utilizing blends with reiterations to deliver multisets of a similar cardinality/estimate as your unique data. By expanding the measure of your training set you can't enhance the model prescient power, however simply diminish the variance, barely tuning the prediction to expected result.

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b) After a weak learner is added to the model, training data are reweighted

Boosting is a two-advance approach, where one first uses subsets of the first data to deliver a progression of averagely performing models and afterward "boosts" their execution by joining them together utilizing a specific cost function.

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