QMB3302 Samenvattingen, Aantekeningen en Examens

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...

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...

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...

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...

features of a model - correct answer represent your inputs problems with hold out method for validation - correct answer model is sensitive to outliers not built on all training data pipelines are useful for the following reasons - correct answer make it easy to change small things in your model (variables to include) help organize code used to clean and treat data make it easy to repeat steps and run multiple models X values are ____________, and y value(s) are something we are try...