Detaillierter Kursinhalt
Introduction to advanced machine learning models• Taxonomy of models• Overview of supervised models• Overview of models to create natural groupingsGroup fields: Factor Analysis and Principal Component Analysis• Factor Analysis basics• Principal Components basics• Assumptions of Factor Analysis• Key issues in Factor Analysis• Improve the interpretability• Factor and component scoresPredict targets with Nearest Neighbor Analysis• Nearest Neighbor Analysis basics• Key issues in Nearest Neighbor Analysis• Assess model fitExplore advanced supervised models• Support Vector Machines basics• Random Trees basics• XGBoost basicsIntroduction to Generalized Linear Models• Generalized Linear Models• Available distributions• Available link functionsCombine supervised models• Combine models with the Ensemble node• Identify ensemble methods for categorical targets• Identify ensemble methods for flag targets• Identify ensemble methods for continuous targets• Meta-level modelingUse external machine learning models• IBM SPSS Modeler Extension nodes• Use external machine learning programs in IBM SPSS ModelerAnalyze text data• Text Mining and Data Science• Text Mining applications• Modeling with text data