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Sunday, 14 November 2021

Machine Learning Multiple Choice Questions with Answers 34

Top 3 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, question bank in machine learning, ensemble machine learning methods, bagging, sampling error, overfitting in decision trees

Machine learning Quiz Questions - Set 34

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1. Which of the following ensemble model helps in reducing variance?

a) Boosting

b) Bagging

c) Stacking

d) Voting

Answer: (b) Bagging

Bagging (also called as Bootstrap Aggregation) is an ensemble method which is the application of Bootstrap procedure to a high variance ML algorithm. Averaging reduces variance. Bagging uses bootstrap to generate L training sets, trains L base-learners using an unstable learning procedure, and then, during testing, takes an average.

What is an ensemble model in machine learning?

An ensemble method is a technique which uses multiple independent similar or different models/weak learners to derive an output or make some predictions.

An ensemble method is a technique that combines the predictions from multiple machine learning algorithms together to make more accurate predictions than any individual model.

 

 

2. Which of the following helps in avoiding overfitting in decision trees?

a) Adding more irrelevant attributes

b) Generating a tree with fewer branches

c) Generating a complete tree then getting rid of some branches

d) All of the above

Answer: (b)Generating a tree with fewer branches and (c) Generating a complete tree then getting rid of some branches

Two approaches to avoiding overfitting are distinguished: pre-pruning (generating a tree with fewer branches than would otherwise be the case) and post-pruning (generating a tree in full and then removing parts of it). Results are given for pre-pruning using either a size or a maximum depth cutoff. A method of post-pruning a decision tree based on comparing the static and backed-up estimated error rates at each node is also described.

We need to remove irrelevant attributes.

 

3. What is sampling error in statistics?

a) Difference between population and parameter

b) Difference between population and sample

c) Difference between sample and mean

d) Difference between sample and parameter

Answer: (b) Difference between population and sample

The sampling error is the difference between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter.

Errors happen when you take a sample from the population rather than using the entire population. In other words, it’s the difference between the statistic you measure and the parameter you would find if you took a census of the entire population.


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Related links:

What is the difference between sequential and parallel ensemble methods?

What is an ensemble model?

How to avoid overfitting in decision tree?

What is sampling error?

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