PPT-Regression, Prediction and Classification

Jacob LaRiviere Terminology Goal is to model outcomes as a function of features Width of is Length of and is   Terminology cont   Feature 1 Obs 1 Obs 1 Terminology cont . Chris Franck. LISA Short Course. March 26, 2013. Outline. Overview of LISA. Overview of CART. Classification tree description. Examples – iris and skull data.. Regression tree description. Examples – simulated and car data. Assumptions on noise in linear regression allow us to estimate the prediction variance due to the noise at any point.. Prediction variance is usually large when you are far from a data point.. We distinguish between interpolation, when we are in the convex hull of the data points, and extrapolation where we are outside.. Alexander Swan & Rafey Alvi. Residuals Grouping. No regression analysis is complete without a display of the residuals to check that the linear model is reasonable.. Residuals often reveal subtleties that were not clear from a plot of the original data.. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models . Part . 4 . – . Prediction. Prediction. Use of the model for prediction. (Classification, Regression). Ryan Shaun . Joazeiro. de Baker. Prediction. Pretty much what it says. A student is using a tutor right now.. Is he gaming the system or not?. . (“attempting to succeed in an interactive learning environment by exploiting properties of the system rather than by learning the material”). Ensemble Methods. Bamshad Mobasher. DePaul University. Ensemble methods. Use a combination of models to increase accuracy. Combine a series of k learned models, . M. 1, . M. 2, …, . Mk. , with the aim of creating an improved model . (Classification, Regression). Ryan Shaun . Joazeiro. de Baker. Prediction. Pretty much what it says. A student is using a tutor right now.. Is he gaming the system or not?. . (“attempting to succeed in an interactive learning environment by exploiting properties of the system rather than by learning the material”). Jacob LaRiviere . Intertemporal Substitution. Cash for Clunkers: Trade in an old car for . well above. market . value if you purchase a new car. . . Mian. and Sufi (2013) showed that although there was a tremendous increase . Overview of Supervised Learning. Outline. Regression vs. Classification. Two . Basic Methods: Linear Least Square vs. Nearest Neighbors. C. lassification via Regression. C. urse of Dimensionality and . Pg 337..345: 3b, 6b (form and strength). Page 350..359: 10b, 12a, 16c, 16e. Homework Turn In…. A straight line that describes how a response variable y changes as an explanatory variable x changes. . Ryan Shaun . Joazeiro. de Baker. Prediction. Pretty much what it says. A student is using a tutor right now.. Is he gaming the system or not?. . (“attempting to succeed in an interactive learning environment by exploiting properties of the system rather than by learning the material”). Jeff Chen. , Abe Dunn, Kyle Hood, . Alex Driessen and Andrea Batch. Motivation. 2. End of. Quarter. Advance. Estimate. Second. Estimate. When source . data are available. When we’d. like it to be available. Machine Learning. Classification. Email: Spam / Not Spam?. Online Transactions: Fraudulent (Yes / No)?. Tumor: Malignant / Benign ?. 0: “Negative Class” (e.g., benign tumor). . 1: “Positive Class” (e.g., malignant tumor). 2. Dr. Alok Kumar. Logistic regression applications. Dr. Alok Kumar. 3. When is logistic regression suitable. Dr. Alok Kumar. 4. Question. Which of the following sentences are . TRUE.  about . Logistic Regression.