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. edu Caltech Pasadena CA 91125 USA Thomas Fuchs fuchscaltechedu Caltech Pasadena CA 91125 USA Piotr Dollar pdollarmicrosoftcom Microsoft Research Redmond WA 98052 USA Pietro Perona peronacaltechedu Caltech Pasadena CA 91125 USA Abstract Boosted decisi edu laltechOyasadenaOljbTTUUj Thomas Fuchs fuchscaltechedu laltechOyasadenaOljbTTUUj Piotr Doll57524ar pdollarmicrosoftcom vicrosoftesearchOedmondOWjbaSUUj Pietro Perona peronacaltechedu laltechOyasadenaOljbTTUUj Abstract koosteddecisiontreesareamong edu Caltech Pasadena CA 91125 USA Thomas Fuchs fuchscaltechedu Caltech Pasadena CA 91125 USA Piotr Dollar pdollarmicrosoftcom Microsoft Research Redmond WA 98052 USA Pietro Perona peronacaltechedu Caltech Pasadena CA 91125 USA Abstract Boosted decisi 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 . Battiti. , Mauro . Brunato. .. The LION Way: Machine Learning . plus.  Intelligent Optimization. .. LIONlab. , University of Trento, Italy, . Apr 2015. http://intelligent-optimization.org/LIONbook. A . decision tree. is a graphical representation of every possible sequence of decision and random outcomes (states of nature) that can occur within a given decision making problem.. A decision tree is composed of a collection of nodes (represented by circles and squares) interconnected by branches (represented by lines).. 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 . CSE 335/435. Resources:. Main: . Artificial Intelligence: A Modern Approach (Russell and . Norvig. ; Chapter “Learning from Examples. ”). Alternatives:. http. ://www.dmi.unict.it/~. apulvirenti/agd/Qui86.pdf. 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 . 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. . Zhiqi. Peng. Key concepts of supervised learning. Objective function:. is training loss, measure how well model fit on training data. is regularization, measures complexity of model.  . Key concepts of supervised learning. Presented by: . Xiaowei. Shang. Background. Gradient . boosting decision tree (GBDT. ) . is a widely-used machine learning algorithm, due to its efficiency, accuracy, and interpretability. GBDT achieves state-of-the-art performances in many machine learning tasks, such as multi-class . 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 (.