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. You are 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.
On-demand duration is 8 hours.
Join our public courses in our Canada facilities. Private class trainings will be organized at the location of your preference, according to your schedule.