Advanced Predictive Modeling Using IBM SPSS Modeler (v18.1.1) Training in Finland

  • Learn via: Classroom
  • Duration: 1 Day
  • Price: From €1,241+VAT
Learn advanced modeling techniques in IBM SPSS Modeler.

This course presents advanced models to predict categorical and continuous targets. Before reviewing the models, data preparation issues are addressed such as partitioning, detecting anomalies, and balancing data. You are first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core fields, referred to as components or factors. The next units focus on supervised models, including Decision List, Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed to combine supervised models and execute them in a single run, both for categorical and continuous targets.



Who Should Attend?

  • Business Analysts
  • Data Scientists
  • Users of IBM SPSS Modeler responsible for building predictive models
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We can host this training at your preferred location.

Prerequisites

  • Familiarity with the IBM SPSS Modeler environment (creating, editing, opening, and saving streams)
  • Familiarity with basic modeling techniques, either through completion of the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler and/or Predictive Modeling for Continuous Targets Using IBM SPSS Modeler, or by experience with predictive models in IBM SPSS Modeler
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What You Will Learn

  • How to prepare data for modeling
  • Reducing data with PCA/Factor
  • Using Decision List to create rulesets for flag targets
  • Advanced supervised models
  • Combining models
  • Establishing supervised models
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Outline

  1. Preparing data for modeling
  • Address general data quality issues
  • Handle anomalies
  • Select important predictors
  • Partition the data to better evaluate models
  • Balance the data to build better models
  1. Reducing data with PCA/Factor
  • Explain the idea behind PCA/Factor
  • Determine the number of components/factors
  • Explain the principle of rotating a solution
  1. Creating rulesets for flag targets with Decision List
  • Explain how Decision List builds a ruleset
  • Use Decision List interactively
  • Create rulesets directly with Decision List
  1. Exploring advanced supervised models
  • Explain the principles of Support Vector Machine (SVM)
  • Explain the principles of Random Trees
  • Explain the principles of XGBoost
  1. Combining models
  • Use the Ensemble node to combine model predictions
  • Improve model performance by meta-level modeling
  1. Finding the best supervised model
  • Use the Auto Classifier node to find the best model for categorical targets
  • Use the Auto Numeric node to find the best model for continuous targets

On-demand duration is 8 hours.

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Contact us for more detail about our trainings and for all other enquiries!

Avaible Training Dates

Join our public courses in our Finland facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

24 huhtikuuta 2025 (1 Day)
Helsinki, Espoo
Classroom / Virtual Classroom
€1,241 +VAT
26 huhtikuuta 2025 (1 Day)
Helsinki, Espoo
Classroom / Virtual Classroom
€1,241 +VAT
10 kesäkuuta 2025 (1 Day)
Helsinki, Espoo
Classroom / Virtual Classroom
€1,241 +VAT
17 kesäkuuta 2025 (1 Day)
Helsinki, Espoo
Classroom / Virtual Classroom
€1,241 +VAT
03 heinäkuuta 2025 (1 Day)
Helsinki, Espoo
Classroom / Virtual Classroom
€1,241 +VAT
05 heinäkuuta 2025 (1 Day)
Helsinki, Espoo
Classroom / Virtual Classroom
€1,241 +VAT
10 heinäkuuta 2025 (1 Day)
Helsinki, Espoo
Classroom / Virtual Classroom
€1,241 +VAT
17 heinäkuuta 2025 (1 Day)
Helsinki, Espoo
Classroom / Virtual Classroom
€1,241 +VAT
Advanced Predictive Modeling Using IBM SPSS Modeler (v18.1.1) Training Course in Finland

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