Schulung - IBM 0A038G - Advanced Predictive Modeling Using IBM SPSS Modeler (v18.1.1)

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

Objective

• Preparing data for modeling 
• Reducing data with PCA/Factor 
• Creating rulesets for flag targets with Decision List 
• Exploring advanced supervised models 
• Combining models 
• Finding the best supervised model