Machine Learning MCQ - Differences between bagging and boosting

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, ensemble learning, bagging, boosting, differences between bagging and boosting, bagging vs boosting

Machine Learning MCQ - Differences between ensemble learning methods - bagging and boosting

< Previous

Next >

 

1. Which among the following are some of the differences between bagging and boosting?

a) In bagging we use the same classification algorithm for training on each sample of the data, whereas in boosting, we use different classification algorithms on the different training data samples

b) Bagging is easy to parallelize whereas boosting is inherently a sequential process

c) In bagging we typically use sampling with replacement whereas in boosting, we typically use weighted sampling techniques

d) In comparison with the performance of a base classifier on a particular data set, bagging will generally not increase the error whereas as boosting may lead to an increase in the error

 

Answer: (b), (c), and (d)   (b) Bagging (Bootstrap Aggregation) is an ensemble learning method which trains multiple models independently in parallel. Boosting is an ensemble learning method which trains each new model such that it focuses on correcting the errors made by the previous model. (c) In the case of Bagging, any element has the same probability to appear in a new data set. Training data subsets are drawn randomly with a replacement for the training dataset. However, for Boosting, the observations are weighted. In Boosting algorithms each classifier is trained on data, taking into account the previous classifiers’ success. Hence, every new training subset comprises the elements that were misclassified by previous models. Misclassified data increases its weights to emphasize the most difficult cases. (d) Boosting can result in an increase in error over a base classifier due to over-emphasis on existing noise data points in later iterations.   Other differences between bagging and boosting Difference Bagging Boosting Base classifiers training They are trained in parallel They are trained in sequential manner. Bias and variance Decreases model’s variance Decreases model’s bias Overfitting problem Solves the problem Increases the problem Weights of the model Models receive equal weights Models are weighed according to their performance. Model building Each model built independently Models are influenced by the performance of the previous models. When to apply If the classifier shows high variance (unstable). If the classifier shows high bias (stable).

 

< Previous

Next >

 

 

************************

Related links:

• Machine learning MCQ home page

• Machine learning TRUE / FALSE questions home page

• Computer science engineering quiz questions and answers page

• Data Warehousing and Data Mining MCQ with answers home
  

What is ensemble learning?

Difference between bagging and boosting ensemble learning techniques in ML

What is bagging?

What is boosting?

Boosting vs bagging

When to use boosting and when to use bagging?

Which is best - bagging or boosting? 

bagging helps in decreasing variance of a model, boosting helps to decrease the bias of a model

bagging works in parallel whereas boosting works in sequential manner

Machine learning solved mcq, machine learning solved mcq

Machine Learning MCQ - Single linkage and complete linkage hierarchical clustering

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, hierarchical clustering, agglomerative clustering, single linkage cluster distance, complete linkage cluster distance, single-link vs complete-link distance calculations

Machine Learning MCQ - Distance between points in single linkage and complete linkage hierarchical clustering methods

< Previous

Next >

 

1. Considering single-link and complete-link hierarchical clustering, is it possible for a point to be closer to the points in other clusters than to the points in its own cluster? If so, in which approach will this tend to be observed?

a) No

b) Yes, single-link clustering

c) Yes, complete-link clustering

d) Yes, both single-link and complete-link clustering

 

Answer: (d) Yes, both single-link and complete-link clustering This is possible in both single-link and complete-link clustering. In the single-link case, an example would be two parallel chains where many points are closer to points in the other chain/cluster than to points in their own cluster. In the complete-link case, this notion is more intuitive due to the clustering constraint (measuring distance between two clusters by the distance between their farthest points).   What is single link clustering? Single link clustering is one of the hierarchical clustering methods. In single linkage (i.e., nearest-neighbor linkage or sometimes referred as MIN), the dissimilarity between two clusters is the smallest dissimilarity between two points in opposite groups. In other words, in single linkage clustering, the inter-cluster distance (the distance between two clusters) is represented by the distance of the closest pair of data objects belonging to different clusters. cluster distance = distance of two closest members in each class   What is complete link clustering? Yet another hierarchical clustering method. In complete linkage (i.e., furthest-neighbor linkage or MAX), dissimilarity between two clusters is the largest dissimilarity between two points in opposite groups. In other words, in complete linkage clustering, the inter-cluster distance (the distance between two clusters) is represented by the distance of the farthest pair of data objects belonging to different clusters. cluster distance = distance of two farthest members   What is the term linkage refers to in hierarchical clustering?  The choice of linkage determines how we measure dissimilarity between groups of points.

 

< Previous

Next >

 

 

************************

Related links:

• Machine learning MCQ home page

• Machine learning TRUE / FALSE questions home page

• Computer science engineering quiz questions and answers page

• Data Warehousing and Data Mining MCQ with answers home
  

What is hierarchical clustering?

Difference between single-linkage and complete-linkage hierarchical clustering in ML

What is single link clustering?

What is complete link clustering?

What does linkage refer to in hierarchical agglomerative clustering?

Machine learning solved mcq, machine learning solved mcq