Tree-Based Machine Learning Methods in SAS Viya
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.
The self-study e-learning includes:
Who should attendPredictive 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
Before attending this course, you should have the following:
This course addresses SAS Viya, SAS Visual Data Mining and Machine Learning software.
Introduction to Decision Trees
|Title||Duration||Access Period||Language||Fee||Add to Cart|
|Tree-Based Machine Learning Methods in SAS Viya (PDF + 30 virtual lab hours)||21.0 hours||180 days from order date||English||2,045 SGD / 2,045 ETA|