Advanced Predictive Modeling Using IBM SPSS Modeler (v18.1.1) SPVC (0E038G) » zur vollständigen Seminarliste
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
2. Reducing data with PCA/Factor
• Explain the idea behind PCA/Factor
• Determine the number of components/factors
• Explain the principle of rotating a solution
3. 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
4. Exploring advanced supervised models
• Explain the principles of Support Vector Machine (SVM)
• Explain the principles of Random Trees
• Explain the principles of XGBoost
5. Combining models
• Use the Ensemble node to combine model predictions
• Improve model performance by meta-level modeling
6. 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
• Business Analysts.
• Data Scientists.
• Users of IBM SPSS Modeler responsible for building predictive models.
• 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.
Das Training findet auf Deutsch statt.