PPT-Information Gain, Decision Trees and Boosting

10701 ML recitation 9 Feb 2006 by Jure Entropy and Information Grain Entropy amp Bits You are watching a set of independent random sample of X X has 4 possible values PXA14 PXB14 PXC14 PXD14. Lecture . 10. Decision Trees. G53MLE . Machine Learning. Dr . Guoping. Qiu. 1. Trees. Node. Root. Leaf. Branch. Path. Depth. 2. Decision Trees. A hierarchical data structure that represents data by implementing a divide and conquer strategy . Chapter 9. Topics. Two-Stage Op-Amps. Gain-Boosting. Input Rage Limitation. Slew Rate. Power Supply Rejection. Noise. Simple Implementation of a Two-Stage Op-Amp. Stage 1. Two-Stage Op-Amp Employing . Image . Denoising. Algorithms. The research leading to these results has received funding from the European Research Council under European Union's Seventh Framework . Program, . ERC Grant agreement no. . Decision Trees. Gavin Brown. www.cs.man.ac.uk/~gbrown. Recap: threshold classifiers. height. weight. Q. Where is a good threshold?. 10 20 30 40 50 60. 1. 0. Also known as “decision stump”. From Decision . David . Mease. & . Abraham . Wyner. What is the Statistical View? . The idea presented in . J. . Friedman, T. Hastie, and R. . Tibshirani. . Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28:337–374, . Tandy Warnow. The University of Texas at Austin. Orangutan. Gorilla. Chimpanzee. Human. From the Tree of the Life Website,. University of Arizona. Phylogeny. (evolutionary tree). Applications. . of Phylogeny Estimation . Admin. Final project. Ensemble learning. Basic idea: . if one classifier works well, why not use multiple classifiers!. Ensemble learning. Basic idea: . if one classifier works well, why not use multiple classifiers!. 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. Chong Ho (Alex) Yu. Problems of bias and variance. The bias is . the . error which results from missing a target. . For . example, if an estimated mean is 3, but the actual population value is 3.5, then the bias value is 0.5. . Florina. . Balcan. 03/18/2015. Perceptron, Margins, Kernels. Recap from last time: Boosting. Works by creating . a series . of challenge datasets . s.t.. . even modest performance on these can . be . Decision trees MARIO REGIN What is a decision tree? General purpose prediction and classification mechanism Emerged at the same time as the nascent fields of artificial intelligence and statistical computation . by Holly Nguyen, Hongyu Pan, Lei Shi, Muhammad Tahir. Showcasing work by . Themis P. Exarchos, Alexandros T. Tzallas, DinaBaga, Dimitra Chaloglou, Dimitrios I. Fotiadis, Sofia Tsouli, Maria Diako. u. How is normal Decision Tree different from Random Forest?. A Decision Tree is a supervised learning strategy in machine learning. It may be used with both classification and regression algorithms. . As the name says, it resembles a tree with nodes. The branches are determined by the number of criteria. It separates data into these branches until a threshold unit is reached. . Want to keep your testosterone levels healthy? Eating these 5 best testosterone boosting foods that are high in nutrients & vitamins.