Table of Contents
- Decision Trees for Predictive Modeling. SAS algorithms. Consider modeling the price of single family homes.
- Modeling With Sas Enterprise Miner torrent. Predictive Modeling with SAS Enterprise Miner, 2nd Edition Predictive Modeling Using Sas Enterprise Miner.
- Chapter 1: Research Strategy
- 1.3 Defining the Target
- 1.4 Sources of Modeling Data
- 1.5 Pre-Processing the Data
- 1.6 Alternative Modeling Strategies
- Chapter 2: Getting Started with Predictive Modeling
- 2.7 Sample Nodes
- 2.8 Tools for Initial Data Exploration
- 2.9 Tools for Data Modification
- 2.10 Utility Nodes
- 2.11 Appendix to Chapter 2
- Chapter 3: Variable Selection and Transformation of Variables
- 3.2 Variable Selection
- 3.3 Variable Selection Using the Variable Clustering Node
- 3.5 Transformation of Variables
- 3.7 Appendix to Chapter 3
- Chapter 4: Building Decision Tree Models to Predict Response and Risk
- 4.2 An Overview of the Tree Methodology in SAS Enterprise Miner
- 4.3 Development of the Tree in SAS Enterprise Miner
- 4.4 A Decision Tree Model to Predict Response to Direct Marketing
- 4.5 Developing a Regression Tree Model to Predict Risk
- 4.6 Developing Decision Trees Interactively
- 4.8 Appendix to Chapter 4
- Chapter 5: Neural Network Models to Predict Response and Risk
- 5.1 Introduction
- 5.2 A General Example of a Neural Network Model
- 5.4 A Neural Network Model to Predict Response
- 5.5 A Neural Network Model to Predict Loss Frequency in Auto Insurance
- 5.6 Alternative Specifications of the Neural Networks
- 5.7 Comparison of Alternative Built-in Architectures of the Neural Network Node
- Chapter 6: Regression Models
- 6.2 What Types of Models Can Be Developed Using the Regression Node?
- 6.3 An Overview of Some Properties of the Regression Node
- 6.4 Business Applications
- Chapter 7: Comparison and Combination of Different Models
- 7.2 Models for Binary Targets: An Example of Predicting Attrition
- 7.3 Models for Ordinal Targets: An Example of Predicting the Risk of Accident Risk
- 7.5 Boosting and Combining Predictive Models
- 7.6 Appendix to Chapter 7
- Chapter 8: Customer Profitability
- Chapter 9: Introduction to Predictive Modeling with Textual Data
- 9.1 Introduction
- 9.2 Retrieving Documents from the World Wide Web
- 9.7 Text Filter Node
- 9.8 Text Topic Node
- 9.9 Text Cluster Node
https://everskin.weebly.com/gemini-pc-camera-drivers.html. Below we provide a list of the objectives that will be tested on the exam.
For more specific details about each objective download the complete exam content guide.
For more specific details about each objective download the complete exam content guide.
During the testing of these objectives;
You will be expected to perform common tasks, such as:
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- Create a new project in Enterprise Miner
- Open an existing project in Enterprise Miner
- Add diagrams to projects in Enterprise Miner
- Create libraries within Enterprise Miner
- Add nodes to diagrams in Enterprise Miner
- Copy nodes within Enterprise Miner
- Connect nodes to create process flows in Enterprise Miner
- Change interactive sampling methods for data exploration
- Work with the Help functionality within Enterprise Miner
Data Sources - 20-25%
- Create data sources from SAS tables in Enterprise Miner
- Explore and assess data sources
- Modify source data
- Prepare data to be submitted to a predictive model
Building Predictive Models - 35-40%
- Describe key predictive modeling terms and concepts
- Build predictive models using decision trees
- Build predictive models using regression
- Build predictive models using neural networks
Predictive Model Assessment and Implementation - 25-30%
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- Use the correct fit statistic for different prediction types
- Use decision processing to adjust for oversampling (separate sampling)
- Use profit/loss information to assess model performance
- Compare models with the MODEL COMPARISON node
- Score data sets within Enterprise Miner
Pattern Analysis - 10-15% (new content)
- Identify clusters of similar data with the CLUSTER and SEGMENT PROFILE nodes
- Perform association and sequence analysis (market basket analysis)
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