book

Tree-Based Machine Learning Methods in SAS Viya

Name: Tree-Based Machine Learning Methods in SAS Viya
Author: Dr. Sharad Saxena
ISBN: 9781954846654

by Dr. Sharad Saxena

February 2022

Intermediate to advanced

364 pages

9h 37m

English

SAS Institute

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About This Book
About the Author
Acknowledgements
Foreword
Chapter 1: Introduction to Tree-Structured Models
IntroductionDecision Tree – What Is It?Types of Decision TreesTree-Based Models in SAS ViyaAnalytics Platform from SASSAS Visual Data Mining and Machine LearningThe Decision Tree Action SetTREESPLIT, FOREST, and GRADBOOST ProceduresDecision Tree, Forest, and Gradient Boosting Tasks and ObjectsIntroducing Model StudioDemo 1.1: Model Studio Introductory FlowQuiz
Chapter 2: Classification and Regression Trees
Classification TreesDecision RegionsPosterior ProbabilitiesDemo 2.1: Building a Default Classification TreeModel ScoringDemo 2.2: Scoring a Decision Tree Model in Model StudioRegression TreesPredicted Values and Partitioned Input SpaceDemo 2.3: Building a Regression Tree Pipeline from SAS Visual StatisticsQuiz
Chapter 3: Growing a Decision Tree
Recursive PartitioningRoot-Node Split and One-Deep SpaceDepth Two and Two-Deep SpaceAlternatives to Recursive PartitioningConstructing Decision Trees InteractivelyDemo 3.1: Interactively Building a Regression Tree in SAS Visual StatisticsFeature Selection Using Split Search Split PointsSplitting on a Nominal InputSplitting on an Ordinal InputSplitting on an Interval InputDemo 3.2: Exploring Split Search Tree Growth OptionsSplitting CriteriaSplitting Criteria Based on ImpuritySplitting Criteria Based on Statistical TestsDemo 3.3: Experimenting with the Splitting Criteria Quiz
Chapter 4: Decision Trees: Strengths, Weaknesses, and Uses
Missing Values in Decision TreesA Simple ExampleMissing Values in Ordinal InputsVariable ImportanceMethods for Computing Variable ImportanceIntroducing the TREESPLIT ProcedureDemo 4.1: Handling Missing Values and Determining Important VariablesStrengths and Weaknesses of Decision TreesSecondary Uses of Decision TreesInitial Data Analysis (IDA) and Exploratory Data Analysis (EDA)Interaction DetectionIdentifying Important VariablesModel InterpretationVariable SelectionNominal Levels ConsolidationDiscretizing Interval InputsMissing Value ImputationStratified ModelingInteractive TrainingQuiz
Chapter 5: Tuning a Decision Tree
Model Complexity and GeneralizationPruning: Getting the Right-Sized TreePre-Pruning OptionsPost-Pruning OptionsPruning RequirementsHonest AssessmentModel Selection CriteriaDemo 5.1: Pruning Decision Trees Using Validation DataCross ValidationDemo 5.2: Performing Cross Validation in a Regression TreeAutotuningDemo 5.3: Comparing Various Tree Settings and PerformanceQuiz
Chapter 6: Ensemble of Trees: Bagging, Boosting, and Forest
Instability in a Decision TreePerturb and Combine (P&C)Ensemble ModelsBaggingBoostingComparison of Tree-Based ModelsForest ModelsCombining Trees in a ForestPerturbing Trees in a ForestSplitting Options You Already KnowMeasuring Variable ImportanceDemo 6.1: Building a Default Forest ModelTuning a Forest ModelTuning Parameters Manually Autotuning HyperparametersThe FOREST ProcedureDemo 6.2: Tuning a Forest ModelQuiz

Chapter 7: Additional Forest Models
Open-Source Random Forest ModelsOpen Source Code Node Demo 7.1: Executing Open-Source Models in SAS ViyaIsolation Forest ModelsDetecting Fraud Using Tree-Based MethodsWhat Is an Isolation Forest?How Do Isolation Forests Detect Anomalies?Fraud ExampleDemo 7.2: Detecting Fraud Using an Isolation Forest in SAS StudioIntroduction to Deep Forest ModelsDeep Forest Actions in SAS ViyaQuiz
Chapter 8: Tree-Based Gradient Boosting Machines
Gradient Boosting ModelsKey Components of Gradient BoostingWeak Learner ModelTree-Splitting Options You Already KnowLoss FunctionNumerical Optimization MethodAdditive Ensemble ModelAvoiding Overfitting in Gradient BoostingDemo 8.1: Building a Default Gradient Boosting Model in Model Studio and Scoring Using ASTORE in SAS StudioComparison of Gradient Boosted Decision Trees with Other Tree-Based ModelsTuning a Gradient Boosting ModelTuning Parameters Manually Autotuning HyperparametersDemo 8.2: Modifying and Autotuning a Gradient Boosting ModelQuiz
Chapter 9: Additional Gradient Boosting Models
Gradient Boosting for Transfer LearningTransfer Learning Using the GRADBOOST ProcedureDemo 9.1: Transfer Learning Using Gradient BoostingTransfer Learning and AutotuningGradient Boosted Poisson and Tweedie Regression TreesDemo 9.2: Building a Gradient Tree-Boosted Tweedie Compound Poisson ModelSAS Gradient Boosting Using Open SourceDemo 9.3: Training and Scoring a SAS Gradient Boosting Model Using PythonQuiz
Appendix A: Practice Case Study
References

Overview

Discover how to build decision trees using SAS Viya!

Tree-Based Machine Learning Methods in SAS Viya covers everything from using a single tree to more advanced bagging and boosting ensemble methods. The book includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forests, and gradient boosted trees. Each chapter introduces a new data concern and then walks you through tweaking the modeling approach, modifying the properties, and changing the hyperparameters, thus building an effective tree-based machine learning model. Along the way, you will gain experience making decision trees, forests, and gradient boosted trees that work for you.

By the end of this book, you will know how to:

build tree-structured models, including classification trees and regression trees.
build tree-based ensemble models, including forest and gradient boosting.
run isolation forest and Poisson and Tweedy gradient boosted regression tree models.
implement open source in SAS and SAS in open source.
use decision trees for exploratory data analysis, dimension reduction, and missing value imputation.

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