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

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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.

 

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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?

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Machine Learning MCQ - Bagging or stacking - which is best for small training data?

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, bagging, boosting, stacking, which is best for small training dataset? why? three ensemble methods in ML, weak learner

Machine Learning MCQ - Bagging vs stacking - Which ensemble method is best suited for small training data?

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1. In case of limited training data, which technique, bagging or stacking, would be preferred, and why?

a) Bagging, because we can combine as many classifier as we want by training each on a different sample of the training data

b) Bagging, because we use the same classification algorithms on all samples of the training data

c) Stacking, because each classifier is trained on all of the available data

d) Stacking, because we can use different classification algorithms on the training data

Answer: (c) Stacking, because each classifier is trained on all of the available data When data is at a premium, we would ideally prefer to train all models on all of the available training data. Stacking methods use a separate model (a meta learner) to combine the results of the base models (weak learners or constituent models). The second difference is that the stacking based models are mostly heterogeneous in nature as they tend to train different kinds (algorithmically different) of base models. Meta learner takes the outputs of base models as input and gives the prediction as to the final output.   What is bagging in machine learning? Bagging is the type of ensemble technique in which a single training algorithm is used on different subsets of the training data where the subset sampling is done with replacement (bootstrap). Once the algorithm is trained on all the subsets, then bagging predicts by aggregating all the predictions made by the algorithm on different subsets.   What is ensemble learning? Ensemble learning is a machine learning paradigm where multiple models (often called “weak learners” or base models) are trained to solve the same problem and combined to get better results. The main hypothesis is that when weak models are correctly combined, we can obtain more accurate and/or robust models.   Three ensemble learning methods Bagging – use of homogeneous weak learners in parallel to learn and then average them for final predictions Boosting – use of homogeneous weak learners in sequential manner to learn and tries to reduce bias on final predictions Stacking – different weak learners are fitted independently from each other and a meta-model is trained on top of that to predict outputs based on the outputs returned by the base models.   What is weak learner? A ‘weak learner’ is any ML algorithm (for regression/classification) that provides accuracy slightly better than random guessing.

 

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Related links:

• Machine learning MCQ home page

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What is ensemble learning in machine learning?

Difference between bagging and stacking ensemble methods in ML

Out of bagging and stacking, which method is suitable for limited training data in  machine learning?

What do we do in bagging ensemble method

What do we do in stacking ensemble method

What is boosting

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