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

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.

Termíny
Target group
Course structure
Prerequisites

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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.

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.