'Decision tree classification' presentation slideshows

Falls Detection using Accelerometry and Barometric Pressure. 10. Author: Tabish Rizvi. Supervisor: Dr. Stephen Redmond. Motivation

Three kinds of learning. Supervised learning Learning some mapping from inputs to outputs Unsupervised learning Given “data”, what kinds of patterns can you find? Reinforcement learning Learn from positive negative reinforcement. Categorical data example.

7.11. 24./26.10. 14.11. Home Exam. 30.10. 21.11. 28.11. Course on Data Mining (581550-4). Intro/Ass. Rules. Clustering. Episodes. KDD Process. Text Mining. Appl./Summary. Course on Data Mining (581550-4). Today 14.11.2001. Today's subject : Classification, clustering

Decision Tree Classifier. 10. 9. 8. 7. 6. 5. 4. 3. 2. 1. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Ross Quinlan. Abdomen Length > 7.1?. Antenna Length. yes. no. Antenna Length > 6.0?. Katydid. yes. no. Katydid. Grasshopper. Abdomen Length. Antennae shorter than body?.

7.11. 24./26.10. 14.11. Home Exam. 30.10. 21.11. 28.11. Course on Data Mining (581550-4). Intro/Ass. Rules. Clustering. Episodes. KDD Process. Text Mining. Appl./Summary. Course on Data Mining (581550-4). Today 14.11.2001. Today's subject : Classification, clustering

We have seen that we can do machine learning on data that is in the nice “flat file” format Rows are objects Columns are features Taking a real problem and “massaging” it into this format is domain dependent, but often the most fun part of machine learning. Let see just one example….

Decision Trees. Classification Continued Dr Eamonn Keogh Computer Science & Engineering Department University of California - Riverside Riverside,CA 92521 eamonn@cs.ucr.edu. Lets review the classification techniques we have seen so far, in terms of decision surfaces. 5. 4. 3. 2. 1. 0.