MIS372 Lecture Notes - Lecture 3: Crowdsourcing, Overfitting, Intrusion Detection System
1 Oct 2018
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Document Summary
These models categorize data points or instances into predefined categories based on some measurable characteristics of the data instances. (i. e. predictors) Predefined categories or classes are unique nominal values of a target attribute: Predictors can be any attribute or characteristics of the data: House size, lot frontage, # of bedrooms, location. # of positive words, # of negative words, length of review: workings of da/classification. Finds a mapping between predictors and the target categories in the data. Requires (usually a large amount of) previously categorized data instances (i. e. , labelled data) Looks into labelled data and finds a mathematical path or algorithmic path from the predictors to the labels. It includes two main steps: training and testing. Discriminant analysis applications: orion health: skin cancer detection. Predictors: mole scans, patient data (e. g. age, gender, etc. ) Classes: likely, not-likely: yelp: image classification. Classes: food, drinks, inside, outside and menu.
