Data Principles week 5 part 2

What is another name for Bagging in the context of machine learning?

• Boosting

• Bootstrap Aggregation

• Algorithm Aggregation

• Decision Aggregation

Bagging is a simple but powerful type of what?

• Regression method

• Ensemble method

• Clustering method

• Neural network

What is typically used in conjunction with Bagging to create a high-variance machine learning algorithm?

• Neural networks

• Support vector machines

• Decision trees

• Linear regression

In Bagging, how can training instances be utilized?

• They can be sampled only once.

• They can be sampled several times for different predictors.

• They can be sampled several times for the same predictor.

• They cannot be sampled; they are used as is.

What is typically used as the aggregation function in bagging ensemble learning for classification?

• Mean

• Median

• Mode

• Standard deviation

What is the aggregation function typically used for in bagging ensemble learning for regression?

• Mean

• Median

• Mode

• Standard deviation

What is the main benefit of using bagging ensemble learning?

• It increases bias and variance.

• It reduces bias but increases variance.

• It increases bias but reduces variance.

• It helps to reduce both bias and variance.

What does the output of each predictor (classifier) in bagging ensemble learning get summarized by?

• A single predictor's output

• The most complex predictor's output

• The aggregation function

• Random selection

What is the main difference between boosting and bagging algorithms?

• Boosting uses weighted averages, while bagging uses simple averages.

• Boosting runs each model independently before aggregating the outputs, while bagging combines models at the start.

• Boosting is about "teamwork" where each model influences the next, while bagging runs each model independently without preference.

• Boosting aggregates weak learners into strong learners, while bagging focuses on strong learners only.

What do boosting algorithms utilize to transform weak learners into strong learners?

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