ISYE-6501 Exam 1 Practical Questions And Well Elaborated Answers.

Algorithm - correct answer a step-by-step procedure designed to carry out a task Change Detection - correct answer Identifying when a significant change has taken place Classification - correct answer Separation of data into two or more categories Classifier - correct answer A boundary that separates data into two or more categories Cluster - correct answer A group of points that are identified as being similar or near each other Cluster Center - correct answer In some clustering algorithms (k-means), the central point of a cluster center (CENTROID) Clustering - correct answer Separation of points into similar or near groupings. 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 (also cumsum) Deep Learning - correct answer Neural Network model with many hidden layers Dimension - correct answer A feature of the data points. EM Algorithm - correct answer Expectation Maximization Algorithm. Algorithm with two steps (often iterated). 1. Finds the function for the expected likelihood of getting the response given current parameters. 2. Finds new parameter values that maximize probability Heuristic - correct answer Algorithm that isn't guaranteed to find the optimal solution K-means - correct answer Clustering algorithm (unsupervised), that works by defining k centroids and then mapping each point to the closest centroid. K-nearest neighbor (K-NN) - correct answer Classification algorithm (supervised), that works by mapping a data point to the k closest neighbors to it. Kernel - correct answer A type of function that computes the similarity between two inputs. thanks to what's sometimes known as the "kernel trick", non-linear classifiers can be found almost as easily as linear ones. Helps represent higher dimensional data sets. Learning - correct answer Finding/discovering new patterns in data that can be applied to new data Machine - correct answer Apparatus that can do something. in ml it often refers to the algorithm and the computer is run on. 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 the set and the classification boundary; Also called separation. Machine Learning - correct answer Use of computer algorithms to learn and discover patterns or structure in data, without being programmed specifically for them. Misclassified - correct answer To put a data point in the wrong category by a classifier Neural Network - correct answer A machine learning model that itself is modeled after the workings of neurons in the brain. 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 point closest to the classifier, among those in the category. Support Vector Machine (SVM) - correct answer Classification Algorithm (Supervised). Uses boundary to separate data into two or more 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 into two or more special 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 (Think K-means but visually represented) Accuracy - correct answer Fraction of data points correctly classified by the model. (TP + TN)/(TP + TN + FP + FN) Confusion Matrix - correct answer Visualization of classification model performance. Diagnostic odds ratio - correct answer Ratio of the odds that a data point in a certain category is correctly classified by the model, to the odds that a data point is not in that category is incorrectly classified by the model; equal to (TP/FN)/(FP/TN) = (TP*TN)/(FP*FN) Fall out (FPR) - correct answer Fraction of data points not in a certain category that are incorrectly classified by the model. (FP/TN+FP). False Negative - correct answer When the model predicts the value is false but the value is actually true False Negative Rate (Miss Rate) - correct answer Fraction of data points that are incorrectly classified by a model. FN/(FN+TP). False Positive - correct answer When the model predicts that the value is true but the value is actually false False omission rate - correct answer FN/(TN+FN) Hit rate (TPR, Sensitivity, Recall) ** - correct answer TP/(TP+FN) Negative likelihood ratio - correct answer Ratio of the fraction of data points in a certain category that are misclassified as not in the category, to the fraction of data points not in the category that are correctly classified as not being in the category. Equal to (1-sensitivity)/specificity = (FN/(FN+TP))/(TN/(TN+FP)) Positive likelihood ratio - correct answer Ratio of the fraction of data points in a certain category that are correctly classified as being in that category, to the fraction of data points not in that category that are incorrectly classified as not being in that category. Equal to sensitivity/(1-specificity) = (TP/(TP+FN))/(TN/(TN+FP)) Negative Predictive Value (NPV) - correct answer Fraction of data points classified as not in a certain category that are really n