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. The electrical cord for plugging into the wall outlet is on the right side of the cart 2 Turn main Power Switch on the back of the cart to the ON position 3 The cart needs to be plugged in to the wall outlet whether syncing or charging the iPads Pla How to build the Mouse Trap Car. Goals & Objectives. . 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. By Peyton W.. Click Here to Continue. Information:. Each question will have 3 answers, click which one you think is correct. . There is no way to lose! If you get a question wrong, you can return back the question to try again.. [slides prises du cours cs294-10 UC Berkeley (2006 / 2009)]. http://www.cs.berkeley.edu/~jordan/courses/294-fall09. Basic Classification in ML. !!!!$$$!!!!. Spam . filtering. Character. recognition. Input . 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. Tandy Warnow. Joint work with . Siavash. . Mirarab. , . Md. S. . Bayzid. , and others. Orangutan. Gorilla. Chimpanzee. Human. From the Tree of the Life Website. ,. . University . of . Arizona. Dates from Lock et al. Nature, 2011. Weifeng Li, Sagar . Samtani. and . Hsinchun. . Chen. Spring 2016. Acknowledgements:. Cynthia . Rudin. , Hastie & . Tibshirani. Michael Crawford – San Jose State University. Pier Luca . Lanzi. Walker Wieland. GEOG 342. Introduction. Isocluster. Unsupervised. Interactive Supervised . Raster Analysis. Conclusions. Outline. GIS work, watershed analysis. Characterize amounts of impervious cover (IC) at spatial extents . 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”). Sultan Almuhammadi ICS 254: Graphs and Trees 1 Graph & Trees Chapters 10-11 Acknowledgement This is a modified version of Module#22 on Graph Theory by Michael Frank Sultan Almuhammadi ICS 254: Graphs and Trees Note “notes” section on each slide. The Problem. Current forms of haulage can move 240 . lbs. over 20 miles per day (at most). This limits . Access to supplies (e.g., food). Economic growth, particularly for perishable goods. 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. Regression Trees. Characteristics of classification models. model. linear. parametric. global. stable. decision tree. no. no. no. no. logistic regression. yes. yes. yes. yes. discriminant. analysis.