ISYE 6501 Practice Test Questions With Correct Answers

ISYE 6501 Practice Test Questions With Correct Answers Algorithm - CORRECT ANSWER Step by step procedure designed to carry out a task Change Detection - CORRECT ANSWER Identifying when a significant change has taken place in a process. Classification - CORRECT ANSWER The separation of data into two or more categories or (a point's classification) the category a data point is put into Classifier - CORRECT ANSWER A boundary that separates the data into two or more categories. Also (more generally) an algorithm that performs classification. Cluster - CORRECT ANSWER A group of points identified as near/similar to each other Cluster Center - CORRECT ANSWER In some clustering algorithms (like k-means clustering) the central point (centroid) of cluster of data points Clustering - CORRECT ANSWER Separation of data points into groups (clusters) based on nearness/similarity to each other. A common form of unsupervised learning CUSUM - CORRECT ANSWER Change detection method that compares observed distribution mean with a threshold level of change. Short for "cumulative sum". Dimension - CORRECT ANSWER A feature of the data points (ex: height or credit score) Heuristic - CORRECT ANSWER Algorithm that isn't guaranteed to find the optimal solution k-Means Algorithm - CORRECT ANSWER Clustering algorithm that defines k clusters of data points, each corresponding to one of k cluster centers selected by the algorithm k-Nearest Neighbor - CORRECT ANSWER Classification algorithm that defines a data points category as a function of the nearest k data points to it. Margin - CORRECT ANSWER For a single point, the distance between the point and the classification boundary. For a set of points, the minimum distance between a point in a set and the classification boundary. (also called the separation) Misclassified - CORRECT ANSWER Put into the wrong category by a classifier. Supervised Learning - CORRECT ANSWER Machine learning where the "correct" answer is known for each data point in the training set Support Vector - CORRECT ANSWER In SVM models, the closest point to the classifier, among those in a category. Support Vector Machine - CORRECT ANSWER Classification algorithm that uses a boundary to separate the data into two or more categories (classes) Unsupervised Learning - CORRECT ANSWER Machine learning where the "correct" answer is not known for the data points in the training set Voronoi Diagram - CORRECT ANSWER Graphical representation of splitting a plane with two or more special points into regions with one special point each, where each region's points are closer to the region's special point than to any other special point. Attribute - CORRECT ANSWER A characteristic or measurement. In the standard tabular format, a column of data