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Building regression models with SAS : a guide for data scientists /
Building Regression Models with SAS: A Guide for Data Scientists teaches data scientists, statisticians, and other analysts who use SAS to train regression models for prediction with large, complex data. Each chapter focuses on a particular model and includes a high-level overview, followed by basic...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cary, NC :
SAS Institute,
2023.
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Rodriguez, Robert N., |e author. | |
| 245 | 1 | 0 | |a Building regression models with SAS : |b a guide for data scientists / |c Robert N. Rodriguez. |
| 250 | |a [First edition]. | ||
| 264 | 1 | |a Cary, NC : |b SAS Institute, |c 2023. | |
| 300 | |a 1 online resource (464 pages) | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a Building Regression Models with SAS: A Guide for Data Scientists teaches data scientists, statisticians, and other analysts who use SAS to train regression models for prediction with large, complex data. Each chapter focuses on a particular model and includes a high-level overview, followed by basic concepts, essential syntax, and examples using new procedures in both SAS/STAT and SAS Viya. Building Regression Models with SAS is an essential guide to learning about a variety of models that provide interpretability as well as predictive performance. | ||
| 505 | 0 | |a Intro -- Contents -- Motivation for the Book -- Audiences for the Book -- Knowledge Prerequisites for the Book -- Software Prerequisites for the Book -- What the Book Does Not Cover -- Acknowledgments -- Introduction -- Model Building at the Crossroads of Machine Learning and Statistics -- Overview of Procedures for Building Regression Models -- Practical Benefits -- When Does Interpretability Matter? -- When Should You Use the Procedures in This Book? -- How to Read This Book -- General Linear Models -- Building General Linear Models: Concepts -- Example: Predicting Network Activity | |
| 505 | 8 | |a Essential Aspects of Regression Model Building -- Notation and Terminology for General Linear Models -- Parameter Estimation -- The Bias-Variance Tradeoff for Prediction -- Model Flexibility and Degrees of Freedom -- Assessment and Minimization of Prediction Error -- Summary -- Building General Linear Models: Issues -- Problems with Data-Driven Model Selection -- Example: Simulation of Selection Bias -- Freedman's Paradox -- Summary -- Building General Linear Models: Methods -- Best-Subset Regression -- Sequential Selection Methods -- Shrinkage Methods -- Summary | |
| 505 | 8 | |a Building General Linear Models: Procedures -- Introduction to the GLMSELECT Procedure -- Specifying the Candidate Effects and the Selection Method -- Controlling the Selection Method -- Which Selection Methods and Criteria Should You Use? -- Comparing Selection Criteria -- Forced Inclusion of Model Effects -- Example: Predicting the Close Rate of Retail Stores -- Example: Building a Model with Forward Selection -- Example: Building a Model with the Lasso and SBC -- Example: Building a Model with the Lasso and Cross Validation -- Example: Building a Model with the Lasso and Validation Data | |
| 505 | 8 | |a Example: Building a Model with the Adaptive Lasso -- Example: Building a Model with the Group Lasso -- Using the REG Procedure for Best-Subset Regression -- Example: Finding the Best Model for Close Rate -- Example: Best-Subset Regression with Categorical Predictors -- Introduction to the REGSELECT Procedure -- Example: Defining a CAS Session and Loading Data -- Example: Differences from the GLMSELECT Procedure -- Example: Building a Model with Forward Swap Selection -- Using the Final Model to Score New Data -- Summary -- Building General Linear Models: Collinearity | |
| 505 | 8 | |a Example: Modeling the Effect of Air Pollution on Mortality -- Detecting Collinearity -- Dimension Reduction Using Variable Clustering -- Ridge Regression -- The Elastic Net Method -- Principal Components Regression -- Partial Least Squares Regression -- Conclusions for Air Pollution Example -- Summary -- Building General Linear Models: Model Averaging -- Approaches to Model Averaging -- Using the GLMSELECT Procedure for Model Averaging -- Bootstrap Model Averaging with Stepwise Regression -- Refitting to Build a Parsimonious Model -- Model Averaging with Akaike Weights -- Summary | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
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