web数据挖掘__3监督学习1.pptx

Chapter 3: Supervised LearningRoad MapBasic conceptsDecision tree inductionEvaluation of classifiersClassification using association rulesNa?ve Bayesian classificationNa?ve Bayes for text classificationSupport vector machinesK-nearest neighborEnsemble methods: Bagging and BoostingSummaryAn example applicationAn emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients. A decision is needed: whether to put a new patient in an intensive-care unit. Due to the high cost of ICU, those patients who may survive less than a month are given higher priority. Problem: to predict high-risk patients and discriminate them from low-risk patients. Another applicationA credit card company receives thousands of applications for new cards. Each application contains information about an applicant, age Marital statusannual salaryoutstanding debtscredit ratingetc. Problem: to decide whether an application should approved, or to classify applications into two categories, approved and not approved. Machine learning and our focusLike human learning from past experiences.A computer does not have “experiences”.A computer system learns from data, which represent some “past experiences” of an application domain. Our focus: learn a target function that can be used to predict the values of a discrete class attribute, e.g., approve or not-approved, and high-risk or low risk. The task is commonly called: Supervised learning, classification, or inductive learning. The data and the goalData: A set of data records (also called examples, instances or cases) described byk attributes: A1, A2, … Ak. a class: Each example is labelled with a pre-defined class. Goal: To learn a classification model from the data that can be used to predict the classes of new (future, or test) cases/instances.An example: data (loan application)Approved or notAn example: the learning taskLearn a classification model from the data Use the model to classify future loan applica