PPT-CART: Classification and Regression Trees

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. L. ou, Rich . Caruana. , Johannes . Gehrke. (Cornell University). KDD, 2012. Presented by: . Haotian. Jiang. 3.31.2015. Intelligible Models for Classification and Regression. Motivation. 2. . G. eneralized Additive Model. Bayes rule. Popular classification methods. Logistic regression . Linear discriminant analysis (LDA)/QDA and Fisher criteria. K-nearest neighbor (KNN). Classification and regression tree (CART). Bagging. Notes on Classification. Padhraic. Smyth. Department of Computer Science. University of California, Irvine. Review. Models that are linear in parameters . b. , e.g.,. y = . b. 0. + . b. Econ 404 – Jacob LaRiviere . –. Guest Lecture Brian Quistorff. May 10, 2017. Agenda. Review CART. Cross-Validation. How apply to heterogeneity. Problems. Causal Tree. Random Forests. Tree Benefits Intuition. 1) (Brief) History of Engineering. 2) Data Analysis in Excel. The following slides are by my colleague . . Dr. Melissa . Hornstein. Chair of Engineering. Hartnell. College. Part 1: History of Engineering. Object-based classifiers. Others. DECISION TREES. Non-parametric approach. Data mining tool used in many applications, not just RS. Classifies data by building rules based on image values. Rules form trees that are multi-branched with nodes and “leaves” or endpoints. Weifeng Li, Sagar . Samtani. and . Hsinchun. . Chen. Spring 2016. Acknowledgements:. Cynthia . Rudin. , Hastie & . Tibshirani. Michael Crawford – San Jose State University. Pier Luca . Lanzi. Machine . Learning . and . Data Mining. Prof. Carolina Ruiz. Department of Computer Science . WPI. Most figures and images in this presentation were obtained from Google Images. Reminder: What is AI?. 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”). 12/8/16. BGU, DNN course 2016. Sources. Main paper. “. Rich . feature hierarchies for accurate object detection and semantic . segmentation. ”, . Ross . Girshick. , Jeff Donahue, Trevor Darrell, . Shrinivas N. Sabale. Crown class. Crown class is a term used to describe the position of an individual tree in the forest canopy.  . In . the definitions below, “general layer of the canopy” refers to the bulk of the tree crowns in the size class or cohort being examined.  . 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. Pablo Aldama, Kristina . Vatcheva. , PhD. School of Mathematical & Statistical Sciences, University of Texas Rio Grande Val. ley. Data mining methods, such as decision trees, have become essential in healthcare for detecting fraud and abuse, physicians finding effective treatments for their patients, and patients receiving more affordable healthcare services (.