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

Detaillierter Kursinhalt

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