Ensemble Machine Learning Techniques 3.1: Basics of Bagging

The video tutorial covers the bagging technique, starting with its basics and mathematical principles. It explores the use of bagging with SVM for predicting movie ratings and introduces the random forest technique for analyzing sonar data. The tutorial also discusses decision trees for predicting birth weight, comparing bagging and random forest results. A detailed explanation of bootstrapping, including sampling with replacement, is provided. The video concludes with a discussion on the properties of bootstrapping, highlighting its advantages for nonlinear models like decision trees.

What is the primary purpose of using the bagging technique with SVM in the context of the video?

• To reduce the complexity of the model

• To improve the accuracy of movie rating predictions

• To increase the speed of data processing

• To simplify the mathematical calculations

How does the random forest technique help in analyzing sonar data?

• By predicting the speed of sound

• By measuring the temperature of the water

• By differentiating between metals and rocks

• By calculating the depth of the ocean

What is the main advantage of using bootstrapping with nonlinear models like decision trees?

• It simplifies the decision boundaries

• It improves the linearity of the model

• It reduces the variance of the model

• It increases the correlation between samples

In bootstrapping, what does 'sampling with replacement' mean?

• Items are selected randomly and returned to the dataset

• Items are selected randomly and not returned

• Each item is selected only once

• Each item is selected in a fixed order

What is the role of the correlation coefficient (rho) in the variance of bootstrap samples?

• It increases the variance of the samples

• It has no effect on the variance

• It decreases the variance if rho is small

• It always increases the correlation between samples