Advanced Predictive Modeling using IBM SPSS Modeler (V15) Eğitimi

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IBM  » IBM SPSS Modeler Eğitimler
Business Analysis  » Business Analysis Eğitimler

Advanced Predictive Modeling using IBM SPSS Modeler (V15) (0A034G)

IBM Course Code: 0E034G

This course teaches a number of advanced modeling techniques to predict categorical and continuous targets, and is meant for users of IBM SPSS Modeler responsible for building predictive (or "classification") models. Before reviewing the various modeling techniques, prerequisites for building successful models are addressed. The next lessons discuss Decision List, Support Vector Machines and Bayes Nets. After having discussed individual models, it is demonstrated how multiple models can be combined to improve the predictive power. Finally, the focus is on how to automate the process of finding the best predictive model. Each lesson is accompanied by demonstrations and learning activities, to acquire hands-on experience.

Who Needs to Attend

This advanced course is for users of IBM SPSS Modeler responsible for building predictive (or "classification") models

Prerequisites

You should:

  • Complete Introduction to IBM SPSS Modeler and Data Mining or experience in analyzing data with IBM SPSS Modeler.
  • Be familiar with basic modeling techniques, either through completion of the courses Classifying Customers using IBM SPSS Modeler, and Predicting Continuous Targets using IBM SPSS Modeler, or by experience with IBM SPSS Modeler.

Follow-On Courses

There are no follow-ons for this course.

Course Outline

Course Introduction

  • Course objectives
  • Course description
  • Course assumptions

Preparing Data for Modeling

  • General data quality issues
  • The Anomaly node
  • The Feature Selection node
  • The Partition node
  • The Balance node

Rule Induction using Decision List

  • Interactive Decision List
  • Direct Decision List

Machine Learning Models

  • Support Vector Machines
  • Bayes Net

Combining Predictive Models

  • The Ensemble node
  • Meta-level modeling
  • Error modeling

Finding the Best Predictive Model

  • The Auto Classifier node
  • The Auto Numeric node

Data Reduction using PCA/Factor

  • Using PCA/Factor to reduce data



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