Workshop
Thursday, June 11, 2015 in Chicago
Room: Salon A4
Supercharging Prediction with
Ensemble Models
Intended Audience:
- Practitioners: Analysts who would like to learn how to build and gain insight from model ensembles using a state-of-the-art data mining software tool.
- Technical Managers: Project leaders and managers who are responsible for developing predictive analytics solutions and want to understand the potential value and limitations of model ensembles.
Knowledge Level: Basic understanding of statistical methods or predictive modeling algorithms
Workshop Description
Once you know the basics of predictive analytics including data exploration, data preparation, modeling building, and model evaluation, what can be done to improve model accuracy? One key technique is the use of model ensembles, which "groups" or "rolls up" models into a single, usually-better model.
Are model ensembles an algorithm or an approach? How can one understand the influence of key variables in the ensembles? Which options affect the ensembles most? This workshop dives into the key ensemble approaches including Bagging, Random Forests, and Stochastic Gradient Boosting. Attendees will learn "best practices" and attention will be paid to learning and experiencing the influence various options have on ensemble models so that attendees will gain a deeper understanding of how the algorithms work qualitatively and how one can interpret resulting models. Attendees will also learn how to automate the building of ensembles by changing key parameters.
Participant background
Participants are expected to know the principles of predictive analytics. This hands-on workshop requires all participants to be involved actively in the model building process, and therefore must be prepared to work independently or in a small team throughout the day. The instructor will help participants understand the application of predictive analytics principles and will help participants overcome software issues throughout the day.
Software
The key concepts covered during this workshop can be applied to many predictive analytics projects regardless of the software employed. The majority of concepts covered during this workshop apply to all predictive analytics projects - regardless of the particular software employed. The software to be used for this workshop has not yet been finalized.
Hardware: Bring Your Own Laptop
Each workshop participant is required to bring their own laptop running Windows; both PCs and Macintoshes running Windows (through Parallels Desktop or Fusion are acceptable). It is strongly recommended that the software be installed prior to the workshop by visiting the Salford Systems booth in the Predictive Analytics World exposition hall and installing the software from CD or USB.
Attendees receive a course materials book and an official certificate of completion at the conclusion of the workshop.
Schedule
- Software installation (if not already installed): 8:30am
- Workshop program starts at 9:00am
- Morning Coffee Break at 10:30 - 11:00am
- Lunch provided at 12:30 - 1:15pm
- Afternoon Coffee Break at 2:30 - 3:00pm
- End of the Workshop: 4:30pm
Instructor
Dean Abbott, President, Abbott Analytics
Dean Abbott is President of Abbott Analytics in San Diego, California. Mr. Abbott has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, risk modeling, text mining, response modeling, survey analysis, planned giving, and predictive toxicology. In addition, Mr. Abbott serves as chief technology officer and mentor for start-up companies focused on applying advanced analytics in their consulting practices.
Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including PAW, KDD, AAAI, IEEE and several data mining software users conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught applied data mining courses for major software vendors, including SPSS-IBM Modeler (formerly Clementine), Unica PredictiveInsight (formerly Affinium Model), Enterprise Miner (SAS), Model 1 (Group1 Software), and hands-on courses using Statistica (Statsoft), Tibco Spotfire Miner (formerly Insightful Miner), and CART (Salford Systems).
