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.
Familiarity with the IBM SPSS Modeler environment (creating, editing, opening, and saving streams).Familiarity with basic modeling techniques, either through completion of the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler and/or Predictive Modeling for Continuous Targets Using IBM SPSS Modeler, or by experience with predictive models in IBM SPSS Modeler.
Business AnalystsData ScientistsUsers of IBM SPSS Modeler responsible for building predictive models
Preparing data for modelingReducing data with PCA/FactorCreating rulesets for flag targets with Decision ListExploring advanced supervised modelsCombining modelsFinding the best supervised model, Cloud Data and AI, Information Architecture Market, AI Tools Decision Optimization