K means clustering - Study guides, Class notes & Summaries

QMB3302 Final UF EXAM SET TEST QUESTIONS AND CORRECT ANSWERS The correct number of clusters in Hierarchical clustering can be determined precisely using approaches such as silhouette scores (True or False) - 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) - ANSWER : False One big difference between the unsupervised approaches in this module, a...

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 ...

Classification problems are commonly solved using what model(s)? - Support Vector Machine Clustering problems are commonly solved using what model(s)? - k-means Response Prediction questions are commonly solved using what model(s)? - -ARIMA -CART -Exponential smoothing -linear regression -logistic regression -Random Forest

True. There exists a convergence proof. - Answer K-means is a clustering algorithm that is guaranteed to converge. False. A delayed reinforcement learning task is one where the optimal solution can only be found by associating incoming rewards with a whole sequence of previous actions, instead of just the latest one. The reward may very well be received in every time step also in delayed reinforcement learning. - Answer The main difference between immediate and delayed reinforcement lea...

ATO2 Navy Advancement EXAM SET TEST QUESTIONS AND CORRECT ANSWERS The correct number of clusters in Hierarchical clustering can be determined precisely using approaches such as silhouette scores (True or False) - 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) - ANSWER : False One big difference between the unsupervised approaches in this mod...

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...

QMB3302 Final UF EXAM SET TEST QUESTIONS AND CORRECT ANSWERS The correct number of clusters in Hierarchical clustering can be determined precisely using approaches such as silhouette scores (True or False) - 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) - ANSWER : False One big difference between the unsupervised approaches in this module, an...

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...

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 subset of training points in the decision functio...

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 ...