Formation IBM SPSS Modeler : Modèles prédictifs avancés (v18.1.1) Advanced Predictive Modeling Using IBM SPSS Modeler (v18.1.1)

Durée 1 jour
Niveau Intermédiaire
Classe à distance

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Référence 0A038G
Éligible CPF Non
Cours officiel IBM

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. The participant is 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.

Public :

This course targets first Business Analysts, Data Scientists, and Users of IBM SPSS Modeler responsible for building predictive models.

Prérequis :

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.

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

Reducing data with PCA/Factor

Explain the idea behind PCA/Factor
Determine the number of components/factors
Explain the principle of rotating a solution

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

Exploring advanced supervised models

Explain the principles of Support Vector Machine (SVM)
Explain the principles of Random Trees
Explain the principles of XGBoost

Combining models

Use the Ensemble node to combine model predictions
Improve model performance by meta-level modeling

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

Date de mise à jour du programme : 21/02/2024

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