Formation IBM SPSS Modeler : Modèles pour cible quantitative (v18.1.1) Predictive Modeling for Continuous Targets Using IBM SPSS Modeler

Durée 1 jour
Niveau Intermédiaire
Classe à distance

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Référence 0A0V8G
Éligible CPF Non

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.

Public :

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.

Prérequis :

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.

Introduction to predicting continuous targets

List three modeling objectives
List two business questions that involve predicting continuous targets
Explain the concept of field measurement level and its implications for selecting a modeling technique
List three types of models to predict continuous targets
Determine the classification model to use

Building decision trees interactively

Explain how CHAID grows a tree
Explain how C&R Tree grows a tree
Build CHAID and C&R Tree models interactively
Evaluate models for continuous targets
Use the model nugget to score records

Building your tree directly

Explain the difference between CHAID and Exhaustive CHAID
Explain boosting and bagging
Identify how C&R Tree prunes decision trees
List two differences between CHAID and C&R Tree

Using traditional statistical models

Explain key concepts for Linear
Customize options in the Linear node
Explain key concepts for Cox
Customize options in the Cox node

Using machine learning models

Explain key concepts for Neural Net
Customize one option in the Neural Net node

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