Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) (0A079G) – Outline

Detailed Course Outline

Introduction to machine learning models• Taxonomy of machine learning models• Identify measurement levels• Taxonomy of supervised models• Build and apply models in IBM SPSS ModelerSupervised models: Decision trees - CHAID• CHAID basics for categorical targets• Include categorical and continuous predictors• CHAID basics for continuous targets• Treatment of missing valuesSupervised models: Decision trees - C&R Tree• C&R Tree basics for categorical targets• Include categorical and continuous predictors• C&R Tree basics for continuous targets• Treatment of missing valuesEvaluation measures for supervised models• Evaluation measures for categorical targets• Evaluation measures for continuous targetsSupervised models: Statistical models for continuous targets - Linear regression• Linear regression basics• Include categorical predictors• Treatment of missing valuesSupervised models: Statistical models for categorical targets - Logistic regression• Logistic regression basics• Include categorical predictors• Treatment of missing valuesSupervised models: Black box models - Neural networks• Neural network basics• Include categorical and continuous predictors• Treatment of missing valuesSupervised models: Black box models - Ensemble models• Ensemble models basics• Improve accuracy and generalizability by boosting and bagging• Ensemble the best modelsUnsupervised models: K-Means and Kohonen• K-Means basics• Include categorical inputs in K-Means• Treatment of missing values in K-Means• Kohonen networks basics• Treatment of missing values in KohonenUnsupervised models: TwoStep and Anomaly detection• TwoStep basics• TwoStep assumptions• Find the best segmentation model automatically• Anomaly detection basics• Treatment of missing valuesAssociation models: Apriori• Apriori basics• Evaluation measures• Treatment of missing valuesAssociation models: Sequence detection• Sequence detection basics• Treatment of missing valuesPreparing data for modeling• Examine the quality of the data• Select important predictors• Balance the data