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PT22052
Training Summary
This course presents advanced models available in IBM SPSS Modeler. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.
Prerequisites
Knowledge of your business requirementsRequired: IBM SPSS Modeler Foundations (V18.2) course (0A069G/0E069G) or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and know the basics of modeling.Recommended: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) course (0A079G/0E079G), or equivalent knowledge or experience with the product about supervised machine learning models (CHAID, CR Tree, Regression, Random Trees, Neural Net, XGBoost), unsupervised machine learning models (TwoStep Cluster), and association machine learning models such as APriori.
Duration
1 Day
Audience
Data scientistsBusiness analystsExperienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software
Course Topics
Introduction to advanced machine learning modelsTaxonomy of modelsOverview of supervised modelsOverview of models to create natural groupingsGroup fields: Factor Analysis and Principal Component AnalysisFactor Analysis basicsPrincipal Components basicsAssumptions of Factor AnalysisKey issues in Factor AnalysisImprove the interpretabilityFactor and component scoresPredict targets with Nearest Neighbor AnalysisNearest Neighbor Analysis basicsKey issues in Nearest Neighbor AnalysisAssess model fitExplore advanced supervised modelsSupport Vector Machines basicsRandom Trees basicsXGBoost basicsIntroduction to Generalized Linear ModelsGeneralized Linear ModelsAvailable distributionsAvailable link functionsCombine supervised modelsCombine models with the Ensemble nodeIdentify ensemble methods for categorical targetsIdentify ensemble methods for flag targetsIdentify ensemble methods for continuous targetsMeta-level modelingUse external machine learning modelsIBM SPSS Modeler Extension nodesUse external machine learning programs in IBM SPSS ModelerAnalyze text dataText Mining and Data ScienceText Mining applicationsModeling with text data, Cloud Data and AI, Information Architecture Market, AI Tools Decision Optimization