Advanced Predictive Modeling Using IBM SPSS Modeler (V18.1.1) Schulung

Please refer to course overview

Course 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

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