ISYE 6501 - Week 1: Introduction and Classification|2023 LATEST UPDATE|100% PASS

Descriptive Question Questions that ask for explanations of what happened Predictive Questions Questions that ask what's going to happen in the future Prescriptive Questions Questions that ask what action or actions would be best General Questions Generic questions on data analytics Modeling A mathematical expression of a real-life situation Classification Putting things into categories Classifiers Separators Data Point A single observation of information Attributes, Features, Covariates or Predictors One column of a Data Point Response or Outcome The data point column that is the answer Structured Data Data that can be described and stored in a structured way Quantitative Data Numeric Data Categorical Data Data point without quantitative meaning Binary Data An attribute that can take only two values Unstructured Data Data attributes that are not easily described and stored Time Series Data Data attributes related to time Support Vector Machine Model Two parallel lines that are as far apart as possible, with classifier in the middle Convex Hull Connected dots around the outside of a set of points Support Vector The Vector point that touches a classification line Hard Separation A perfect classification of known data Soft Separation Adds a multiplier to amplify the penalty of classification error Scaling Data Adjusting scales so that orders of magnitude are approximately the same Kernel Methods An SVM model that uses nonlinear classifiers Logistic Regression Models Method to estimate probabilities of occurrence Scale to the same interval Scaling data factors to have the same scale ie. 0-1 Scale to a Normal Distribution Standardization - Non linear method to Scale to a mean of zero and a standard deviation of one K-Nearest Neighbor Classification (KNN model) Classification method for models with mor