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