PETE 3061 Final Test Questions & Answers 2024/2025
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Course
PETE 3061
Institution
PETE 3061
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...
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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