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PETE 3061 Final Test Questions & Answers 2024/2025
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PETE 3061 Final Test Questions & Answers 2024/2025 Support Vector Machine (SVM) - ANSWERS- method of supervised learning this is used for classification and regression of data - this method creates a hyperplane and uses it as a decision boundary to classify data - the support vectors are the...

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  • September 10, 2024
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  • 2024/2025
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PETE 3061 Final Test Questions &
Answers 2024/2025

Support Vector Machine (SVM) - ANSWERS- method of supervised learning this is used for classification
and regression of data

- this method creates a hyperplane and uses it as a decision boundary to classify data

- the support vectors are the closest data points to the hyperplane

- if data is nonlinear, kernel can be used to turn data linear to make the hyperplane

- supervised learning



advantages of Support Vector Machine (SVM) - ANSWERS- compact models, take up very little memory

- once model is trained, predication fast is very fast

- data doesn't have to be linear

- works well with high dimensional data



disadvantages of Support Vector Machine (SVM) - ANSWERS- large number of training samples,
computational cost can be prohibitive

- results strongly depend on c-value



Random Forest - ANSWERS- method of supervised learning that is used for classification and regression
of data

- creates an ensemble of randomized decision trees

- makes a collection of unrelated decision trees and merges them together to reduce variance and
increase accuracy of prediction

- supervised learning



advantages of Random Forest - ANSWERS- extremely flexible, performs well on tasks that are underfit by
other estimators

- training and prediction are very fast

, disadvantages of Random Forest - ANSWERS- results are not easily interpretable

- easily overfit



Decision Tree - ANSWERS- a decision support tool that uses a tree-like model of decisions and their
possible consequences

- ask a series of questions designed to zero in on classifications

- general idea is to (a) split predictor space into nested rectangular regions and (b) within each region,
predict the response

- supervised learning



advantages of Decision Tree - ANSWERS- binary splitting makes it very efficient



disadvantages of Decision Tree - ANSWERS- easily overfit; its very easy to go too deep in the tree



Supervised Learning - ANSWERS- a supervised learning algorithm takes data that has already been
labeled or categorized and tries to predict outcomes for unforseen data



examples of Supervised Learning - ANSWERS- Support Vector Machine (SVM)

- Random Forest

- Decision Tree



Unsupervised Learning - ANSWERS- a machine learning technique in which the data sets are not broken
up into certain categories yet, so they are broken down and organized

- purpose is to discover patterns and information that was not previously detected

- where the issues of redundancy among the independent variables and possible reduction in data
dimensionally are first addressed



examples of Unsupervised Learning - ANSWERS- K Means Clustering

- Hierarchical Clustering

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