PPT-Predictive Analytics: Regression & Classification

Weifeng Li Sagar Samtani and Hsinchun Chen Spring 2016 Acknowledgements Cynthia Rudin Hastie amp Tibshirani Michael Crawford San Jose State University Pier Luca Lanzi. 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. Greg Cox. Richard Shiffrin. Continuous response measures. The problem. What do we do if we do not know the functional form?. Rasmussen & Williams, . Gaussian Processes for Machine Learning. http://www.gaussianprocesses.org/. Brian Z. Brown, FCAS, MAAA. Principal and Consulting Actuary. Stan Smith. Predictive Analytics Consultant . April 29, 2016. Advancements In Reserving. Use of stochastic methods.. . Advancements in computing power have allowed for more sophisticated reserving methodologies. December 2013, Jakub Miarka, University of Leeds. RapidMiner. Formerly called . YALE. (Yet Another Language Environment). Environment for . machine learning, data and text mining, predictive and business analytics. Class 5. Tony Cox. tcoxdenver@aol.com. . University of Colorado at Denver. Course web site: . http://cox-associates.com/6330/. . What is a predictive model?. “The probability that X will happen is p” is a predictive model. Prof Sunil . Wattal. Agenda. Introductions. Intro to Data Analytics. Course Logistics. Overview of Topics. Setting up SAS EM. Data Analytics. McKinsey Report. s. hortage of 1.5 million analytics individuals in US. How U.S. companies can improve ERM by using Advanced techniques developed for solvency II and emerging predictive analytics methods. Howard Zail, FSA, FFA, MAAA. Partner, . Elucidor. , LLC. hzail@elucidor.com. Crafton Hills College. Researching:. Alpha to Zeta. Session Objectives. Participants will be able to:. Apply proper controls and create a dataset for a research study. Evaluate multiple statistical analyses, such as statistical and practical significance, logistic regression, and segmentation modeling, for appropriateness to the study. is the use of:. data, . information technology, . statistical analysis, . quantitative methods, and . mathematical or computer-based models . to help managers gain improved insight about their business operations and . Realized Variation . and . Realized Semi-Variance . in the Pharmaceuticals Sector. Haoming. Wang. 2/27/2008. Introduction. Want to examine predictive regressions for realized variance and realized semi-variance (variance caused by negative returns).. Pregnant: . Predicting . T. he Future Of Predictive Analytics In Healthcare. Dale Sanders . SVP Strategy . Health Catalyst. To . what degree is your organization using predictive analytics to improve care were reduce cost?. C. ultural . E. ntrepreneurship. @. andyhamflett. WHY AM I HERE?. DATA = BAD. DATA = BAD. DATA = BAD. DATA = MISUNDERSTOOD?. SESSION OUTLINE. SESSION OUTLINE. SESSION OUTLINE. BIG DATA – QUÉ?. @AAM_Associates @andyhamflett . 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. Tanya Scott. Director, Business Analytics Department. Assoc. Dean, Business & Public Services . Dr. Manju Shah. Assoc. Department Head, Business Analytics Department. Wake Technical Community College .