Tree-Based Machine Learning Methods in SAS® Viya®
VBBF : VBBF01
Decision trees and tree-based ensembles are supervised learning models used for problems involving classification and regression. This course covers everything from using a single tree to more advanced bagging and boosting ensemble methods in SAS Viya. The course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forest models, and gradient boosting models. The course also explains isolation forest (an unsupervised learning algorithm for anomaly detection), deep forest (an alternative for neural network deep learning), and Poisson and Tweedie gradient boosted regression trees. In addition, many of the auxiliary uses of trees, such as exploratory data analysis, dimension reduction, and missing value imputation, are examined, and running open source in SAS and running SAS in open source are demonstrated for tree-based ensemble models.
Learn How To
- Build tree-structured models, including classification trees and regression trees.
- Use the methodology for growing, pruning, and assessing decision trees.
- Build tree-based ensemble models, including forest and gradient boosting.
- Run isolation forest and Poisson and Tweedie gradient boosted regression tree models.
- Provide an introduction to deep forest models.
- Implement open source in SAS and SAS in open source.
- Use decision trees for exploratory data analysis, dimension reduction, and missing value imputation.
Who Should Attend
Predictive modelers and data analysts who want to build decision trees and ensembles of decision trees using SAS Visual Data Mining and Machine Learning in SAS Viya
Prerequisites
Before attending this course, you should have the following:
- An understanding of basic statistical concepts. You can gain this knowledge from the SAS Visual Statistics in SAS Viya: Interactive Model Building course.
- Familiarity with SAS Visual Data Mining and Machine Learning software. You can gain this knowledge from the Machine Learning Using SAS Viya course.
SAS Products Covered
SAS Machine Learning;SAS Viya
Course Outline
Introduction to Decision Trees
- Tree-structured models.
- Tree-based models in SAS Viya.
- Regression trees.
- Recursive partitioning.
- Split search.
- Splitting criteria.
- Missing values and variable importance.
- Pruning.
- Cross validation.
- Autotuning.
- Secondary uses of decision trees.
- Bagging and boosting.
- Forest models.
- Open-source random forest models.
- Isolation forest models.
- Introducing deep forest models.
- Overview of gradient boosting.
- Tuning a gradient boosting model.
- Gradient boosting for transfer learning.
- Gradient boosted Poisson and Tweedie regression trees.
- SAS gradient boosting using open source.
- Data exploration.
- Creating decision trees.
- Out-of-time testing.
- Creating tree-based ensemble models.
- Hyperparameter tuning.
- Model comparison.
Live Class Schedule
Duration: 21 hours
Step into our live classes and experience a dynamic learning environment where you can ask questions, share ideas, and connect with your instructor and classmates. With on-demand lab hours, you can explore the material at your own pace. Our globally acclaimed instructors will motivate you to think bigger, so you can take what you've learned and achieve your biggest goals.
This course isn't publicly scheduled, but private training and mentoring may be available. Contact us to explore options.
Private Training
Get training tailored specifically for your team, led by expert SAS instructors. Choose from virtual sessions, or training at your location (or ours). Perfect for teams seeking a customized curriculum and plenty of interaction with a SAS specialist. We'll schedule it at a time that works for you.
Mentoring Services
Take your training to the next level with personalized mentoring. While private training offers structured coursework, mentoring provides hands-on, real-time support from a subject matter expert. As you work with your own data, you'll receive expert guidance to help you uncover insights, unlock the full potential of your data, and make faster progress. Perfect for those looking to apply what they’ve learned and see quicker results.