K means clustering Samenvattingen, Aantekeningen en Examens
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QMB3302 FINAL UF EXAM 2024 WITH ACCURATE ANSWERS
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The correct number of clusters in Hierarchical clustering can be determined precisely using approaches such as silhouette scores (True or False) - correct answer False 
 
In K Means clustering, the analyst does not need to determine the number of clusters (K), these are always derived analytically using the kmeans algorithm. (True or False) - correct answer False 
 
One big difference between the unsupervised approaches in this module, and the supervised approaches in prior modules: Unsupervised...
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Summary of all Datacamp modules for Data Science Skills with import codes and steps to perform analysis (325243-M-6))
- Samenvatting • 42 pagina's • 2023
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This document contains a summary of all datacamp modules with all important codes, functions, methods and steps to perform certain analysis. Useful for pacticing before the exam.
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ISYE6501: MIDTERM 1 LATEST 2023 RATED A
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ISYE6501: MIDTERM 1 LATEST 2023 RATED A 
Matching models/methods to categories (cusum and pca = NONE) 
Select all of the following models that are designed for use with attribute/feature data (i.e., not time-series data): k-nearest-neighbor, PCA, k-means, logistic regression, linear regression, random forest, SVM's 
Classification models CART, k-nearest-neighbor, logistic regression, random forest, support vector machine 
Clustering k-means 
Response prediction ARIMA, Exponential smoothing, Lin...
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ISYE 6501 - Midterm 1 ALL SOLUTION & ANSWERS 100% CORRECT SPRING FALL-2023/24 EDITION GUARANTEED GRADE A+
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What do descriptive questions ask? 
 
What happened? (e.g., which customers are most alike) 
 
What do predictive questions ask? 
 
What will happen? (e.g., what will Google's stock price be?) 
 
What do prescriptive questions ask? 
 
What action(s) would be best? (e.g., where to put traffic lights) 
 
What is a model? 
 
Real-life situation expressed as math. 
 
What do classifiers help you do? 
 
differentiate 
 
What is a soft classifier and when is it used? 
 
In some cases, ther...
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ISYE-6501 Exam 1 QUESTIONS & CORRECT ANSWERS
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ISYE-6501 Exam 1 QUESTIONS & 
CORRECT ANSWERS 
Algorithm - ANSWER a step-by-step procedure designed to carry 
out a task 
Change Detection - ANSWER Identifying when a significant 
change has taken place 
Classification - ANSWER Separation of data into two or more 
categories 
Classifier - ANSWER A boundary that separates data into two or 
more categories 
Cluster - ANSWER A group of points that are identified as being 
similar or near each other 
Cluster Center - ANSWER In some clustering ...
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ISYE 6501 Final Questions and Answers Already Passed
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ISYE 6501 Final Questions and Answers Already Passed Support Vector Machine A supervised learning, classification model. Uses extremes, or identified points in the data from which margin vectors are placed against. The hyperplane between these vectors is the classifier 
SVM Pros/Cons Pros: It works really well with a clear margin of separation It is effective in high dimensional spaces. It is effective in cases where the number of dimensions is greater than the number of samples. It uses a subse...
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ISYE-6501 Exam 1 QUESTIONS & CORRECT ANSWERS
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ISYE-6501 Exam 1 QUESTIONS & 
CORRECT ANSWERS 
Algorithm - ANSWER a step-by-step procedure designed to carry 
out a task 
Change Detection - ANSWER Identifying when a significant 
change has taken place 
Classification - ANSWER Separation of data into two or more 
categories 
Classifier - ANSWER A boundary that separates data into two or 
more categories 
Cluster - ANSWER A group of points that are identified as being 
similar or near each other 
Cluster Center - ANSWER In some clustering ...
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Data Science 11 - Clustering algorithms
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Data Science 11 - Clustering algorithms 
k-Means and variants; Initialization: 
• Randomly chooses k points from X used as the initial means 
• k-Means++: Pick initial means, such that they are uniformly distributed in the space. 
This leads to faster convergence 
k-Means and variants; Representatives: 
• k-Medoids or Partitioning Around Medoids (PAM): The cluster 
representatives are medoids (objects from X). Only the distance between objects is 
needed 
Problems with k-Means: 
• Cluste...
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ISYE-6501 Exam 1 QUESTIONS & CORRECT ANSWERS
- Tentamen (uitwerkingen) • 19 pagina's • 2023
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ISYE-6501 Exam 1 QUESTIONS & 
CORRECT ANSWERS 
Algorithm - ANSWER a step-by-step procedure designed to carry 
out a task 
Change Detection - ANSWER Identifying when a significant 
change has taken place 
Classification - ANSWER Separation of data into two or more 
categories 
Classifier - ANSWER A boundary that separates data into two or 
more categories 
Cluster - ANSWER A group of points that are identified as being 
similar or near each other 
Cluster Center - ANSWER In some clustering ...
-
QMB3302 Final UF Full Exam with Guaranteed Correct Answers
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The correct number of clusters in Hierarchical clustering can be determined precisely using approaches such as silhouette scores (True or False) - correct answer False 
 
In K Means clustering, the analyst does not need to determine the number of clusters (K), these are always derived analytically using the kmeans algorithm. (True or False) - correct answer False 
 
One big difference between the unsupervised approaches in this module, and the supervised approaches in prior modules: Unsupervised...