Tree-Based Machine Learning Methods in SAS(R) Viya(R)

Course code: VBBF35

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 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 Tweedy 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.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.
1 080 EUR

1 307 EUR including VAT

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 21 hours

Language: en

Price without VAT: 1 080 EUR


Starting date: Upon request

Type: Upon request

Course duration: 21 hours

Language: en

Price without VAT: 1 800 EUR


Type Course
Language Price without VAT
Upon request E-learning 21 hours en 1 080 EUR Register
Upon request Upon request 21 hours en 1 800 EUR Register
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Target group

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

Course structure

Introduction to Decision Trees

  • Tree-structured models.
  • Recursive partitioning.

Growing a Decision Tree

  • Split search.
  • Splitting criteria.
  • Missing values and variable importance.

Preventing Overfitting in Decision Trees

  • Pruning.
  • Subtree methods.
  • Assessing decision trees.

Ensembles of Trees: Bagging, Boosting, and Forest

  • Ensembling.
  • Bagging.
  • Forest models.
  • Tree splitting in forests.
  • Hyperparameter tuning.
  • Model interpretability.

Tree-Based Gradient Boosting Machines

  • Boosting.
  • Gradient boosting.
  • Tree splitting in gradient boosting.
  • Early stopping.
  • Hyperparameter tuning.
  • Model interpretability.

A Practice Case Study

  • Data exploration.
  • Class levels consolidation.
  • Variable selection/dimension reduction.
  • Imputation.
  • Prediction profiling.


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.
  • Do you need advice or a tailor-made course?


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