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[Workshops]
Workshop on Art and Practice of Regression Trees and Forests
 
Tuesday 12 June 2018, 09:00am - 01:00pm
NUS Kent Ridge campus, Building S16, Seminar Room #06-118
 

REGISTER NOW before 11th June 2018, 12noon.

Registration Form in PDF Format here

Registration Form in WORD Format here

 

 

Regression tree and forest methods have greatly improved in the last decade. Their ease of use, prediction accuracy, execution speed, and interpretability make them essential tools for machine learning and data analysis. The workshop, which may be subtitled “Classification and Regression Trees by Example," teaches how to use the tools effectively and efficiently in practice.

The target audience is statisticians, data scientists, and researchers in business, government, industry, and academia, who  have experience with linear and logistic regression. It should be particularly useful for those who need to explore and analyse complex datasets with many variables and missing values and who want to learn to use the free classification and regression tree software.

The workshop is led by Wei-Yin Loh, who is professor of statistics at the University of Wisconsin, Madison, and an ASA and IMS Fellow. He has been using and developing lassification and regression algorithms and software for more than 30 years. He is the sole developer of the GUIDE algorithm and co-developer of the FACT, QUEST, CRUISE, and LOTUS algorithms. He regularly teaches semester-long undergraduate and graduate courses on the subject at his university and has given one- and multi-day short courses at professional meetings, ASA chapters, biopharma companies, and overseas academic instituitions. 

Specially selected real datasets are used to motivate and illustrate particular difficulties faced by traditional techniques and how they are overcome and solved in new ways by tree methods. Live demos of free software are interwoven in the presentation to encourage hands-on training. No commercial software is required.

 

Topics include:

1: Estimation of population mean income from a consumer expenditure survey

 

2: Classification of peptide sequences

 

3: Birthweight data

 

4: College tuition data

 

5: Hourly wages of high-school dropouts

 

6: Alzheimer's disease data

 

7: Breast cancer randomized trial

 

8: Type II diabetes randomized trial

 

9: Mortality from cardiovascular disease

 

10: Post-selection inference

 

........More details here