Detailed 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 basic ideas behind PCA/Factor • Customize two options in the PCA/Factor node 3. Using Decision List to Create Rulesets • Explain how Decision List builds a ruleset • Use Decision List interactively • Create rulesets directly with Decision List 4. Exploring advanced predictive models • Explain the basic ideas behind SVM • Customize two options in the SVM node • Explain the basic ideas behind Bayes Net • Customize two options in the SVM node 5. Combining Models • Use the Ensemble node to combine model predictions • Improve the model performance by meta-level modeling 6. Finding the Best Predictive Model • Find the best model for categorical targets with AutoClassifier • Find the best model for continuous targets with AutoNumeric