This course presents advanced models to predict categorical and continuous targets. Before reviewing the models, data preparation issues are addressed such as partitioning, detecting anomalies, and balancing data. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core fields, referred to as components or factors. The next units focus on supervised models, including Decision List, Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed to combine supervised models and execute them in a single run, both for categorical and continuous targets.
1. Preparing data for modeling
2. Reducing data with PCA/Factor
3. Creating rulesets for flag targets with Decision List
4. Exploring advanced supervised models
5. Combining models
6. Finding the best supervised model