Predictive Modeling for Continuous Targets Using IBM SPSS Modeler (v18.1.1) - 0A0V8G

PT21441
Training Summary
This course provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. Students are introduced to machine learning models, such as Neural Networks. Business use case examples include: predicting the length of subscription for newspapers, telecommunication, and job length, as well as predicting insurance claim amounts.
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 (v18.1.1) is recommended.
Duration
1 Day
Audience
IBM SPSS Modeler Analysts who have completed the Introduction to IBM SPSS Modeler and Data Mining course who want to become familiar with the modeling techniques available in IBM SPSS Modeler to predict a continuous target.
Course Topics
1: Introduction to predictive models for continuous targetsList three modeling objectivesList two business questions that involve predicting continuous targetsExplain the concept of field measurement level and its implications for selecting a modeling techniqueList three types of models to predict continuous targetsDetermine the classification model to use� �2: Building decision trees interactivelyExplain how CHAID grows a treeExplain how C&R Tree grows a treeBuild CHAID and C&R Tree models interactivelyEvaluate models for continuous targetsUse the model nugget to score records�3: Building decision trees directlyCustomize two options in the CHAID nodeCustomize two options in the C&R Tree nodeList one difference between CHAID and C&R Tree� �4. Using traditional statistical modelsExplain key concepts for LinearCustomize options in the Linear nodeExplain key concepts for CoxCustomize options in the Cox node� �5: Using machine learning modelsExplain key concepts for Neural NetCustomize one option in the Neural Net nodeCloud & Data PlatformData and AIInformation Architecture MarketAI Tools & Runtime PortfolioDecision Optimization

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