Schulung - IBM 0A0U8G - Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18.1.1)

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DURCHFÜHRUNG MIT TERMIN
Nr.
30378

Dauer
8h00

Preis
800,00 € netto
952,00 € inkl. 19% MwSt.

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Overview

This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

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Zielgruppe

Wer sollte teilnehmen:

Zielgruppe

Audience

• Analytics business users who have completed the Introduction to IBM SPSS Modeler and Data Mining course and who want to become familiar with analytical models to predict a categorical field (yes/no churn, yes/no fraud, yes/no response to a mailing, pass/fail exams, yes/no machine break-down, and so forth).

Voraussetzungen

Prerequisites

• Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and a basic knowledge of modeling.
• Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1) is recommended.
 

Trainingsprogramm

Trainingsprogramm

Course Outline

1: Introduction to predictive models for categorical targetsIdentify three modeling objectivesExplain the concept of field measurement level and its implications for selecting a modeling techniqueList three types of models to predict categorical targets2: Building decision trees interactively with CHAIDExplain how CHAID grows decision treesBuild a customized model with CHAIDEvaluate a model by means of accuracy, risk, response and gainUse the model nugget to score records3: Building decision trees interactively with C&R Tree and QuestExplain how C&R Tree grows a treeExplain how Quest grows a treeBuild a customized model using C&R Tree and QuestList two differences between CHAID, C&R Tree, and Quest4: Building decision trees directlyCustomize two options in the CHAID nodeCustomize two options in the C&R Tree nodeCustomize two options in the Quest nodeCustomize two options in the C5.0 nodeUse the Analysis node and Evaluation node to evaluate and compare modelsList two differences between CHAID, C&R Tree, Quest, and C5.05: Using traditional statistical modelsExplain key concepts for DiscriminantCustomize one option in the Discriminant nodeExplain key concepts for LogisticCustomize one option in the Logistic node6: Using machine learning modelsExplain key concepts for Neural NetCustomize one option in the Neural Net node

Objective

1: Introduction to predictive models for categorical targets Identify three modeling objectives Explain the concept of field measurement level and its implications for selecting a modeling technique List three types of models to predict categorical targets  

2: Building decision trees interactively with CHAID Explain how CHAID grows decision trees Build a customized model with CHAID Evaluate a model by means of accuracy, risk, response and gain Use the model nugget to score records  

3: Building decision trees interactively with C&R Tree and Quest Explain how C&R Tree grows a tree Explain how Quest grows a tree Build a customized model using C&R Tree and Quest List two differences between CHAID, C&R Tree, and Quest

 

4: Building decision trees directly Customize two options in the CHAID node Customize two options in the C&R Tree node Customize two options in the Quest node Customize two options in the C5.0 node Use the Analysis node and Evaluation node to evaluate and compare models List two differences between CHAID, C&R Tree, Quest, and C5.0  

5: Using traditional statistical models Explain key concepts for Discriminant Customize one option in the Discriminant node Explain key concepts for Logistic Customize one option in the Logistic node  

6: Using machine learning models Explain key concepts for Neural Net Customize one option in the Neural Net node

Schulungsmethode

Schulungsmethode

presentation, discussion, hands-on exercises

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Weitere Informationen

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Schulung - IBM 0A0U8G - Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18.1.1)