PPT-Decision Trees: Another Example

Training Set Play Tennis Weak Rain Mild High Weak No Rain Mild High Weak No Overfitting Underfitting in Decision Trees Overfitting vs Underfitting. Figure 1 Analysis Figure 2 brPage 3br dI dT Object Moment of Inertia I kgm Period For One Oscillation s Table 1 Figure 3 I I brPage 4br I T T I Conclusion References T state 8712X action or input 8712U uncertainty or disturbance 8712W dynamics functions XUW8594X w w are independent RVs variation state dependent input space 8712U 8838U is set of allowed actions in state at time brPage 5br Policy action is function KarlineSoetaert3example(image3D)example(contour3D)example(colkey)example(jet.col)example(perspbox)example(mesh)example(trans3D)example(plot.plist)example(ImageOcean)example(Oxsat)2.Functionsimage2Dand trees (cont.). If we pick the adjacent nucleotide, what gene tree do we expect?. A. C. B. A-B coalescence. AB-C coalescence. Split 2. Split 1. If we pick a nucleotide from a distant part of the genome, what gene tree do we expect?. Example decision: End point planning Example decision: Random Dot Coherent motion paradigm Example decision: Random Dot Coherent motion paradigm Example decision: Random Dot Coherent motion paradigm W Example:The8-puzzle Example:The8-puzzle(cont'd)Istates:integerlocationsoftiles(ignoreintermediatepositions)Iactions:moveblankleft,right,up,down(ignoreunjammingetc.)Igoaltest:=goalstate(given)Ipathcost Training Set:. Play Tennis?. Weak. Rain Mild High Weak No. Rain Mild High Weak No. Decision Trees: Another Example. Training Set:. Play Tennis?. Sunny. Weak. 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. Statin Choice Decision Aid Share-Decision Making SCIP Shared Decision Making Shared Decision Making Glasziou and Haynes ACP JC 2005 Promote a process where patients and clinicians make a choice together. 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 Decision Trees and Decision Tables 2 Decision Trees and Decision Tables Often our problem solutions require decisions to be made according to two or more conditions or combinations of conditions Decision trees represent such decision as a 6. 9. 2. 4. 1. 8. <. >. =. © 2014 Goodrich, Tamassia, Goldwasser. Presentation for use with the textbook . Data Structures and Algorithms in Java, 6. th. edition. , by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser, Wiley, 2014. 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. . 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 (.