This course provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. Students are introduced to machine learning models, such as Neural Networks. Business use case examples include: predicting the length of subscription for newspapers, telecommunication, and job length, as well as predicting insurance claim amounts.

Argomenti

1: Introduction to predicting continuous targets- List three modeling objectives- List two business questions that involve predicting continuous targets- Explain the concept of field measurement level and its implications for selecting a modeling technique- List three types of models to predict continuous targets- Determine the classification model to use2: Building decision trees interactively- Explain how CHAID grows a tree- Explain how C&R Tree grows a tree- Build CHAID and C&R Tree models interactively- Evaluate models for continuous targets- Use the model nugget to score records3: Building your tree directly- Explain the difference between CHAID and Exhaustive CHAID- Explain boosting and bagging- Identify how C&R Tree prunes decision trees- List two differences between CHAID and C&R Tree4: Using traditional statistical models- Explain key concepts for Linear- Customize options in the Linear node- Explain key concepts for Cox- Customize options in the Cox node5: Using machine learning models- Explain key concepts for Neural Net- Customize one option in the Neural Net node

Obiettivi

1: Introduction to predictive models for continuous targets - List three modeling objectives - List two business questions that involve predicting continuous targets - Explain the concept of field measurement level and its implications for selecting a modeling technique - List three types of models to predict continuous targets - Determine the classification model to use

2: Building decision trees interactively - Explain how CHAID grows a tree - Explain how C&R Tree grows a tree - Build CHAID and C&R Tree models interactively - Evaluate models for continuous targets - Use the model nugget to score records

3: Building decision trees directly - Customize two options in the CHAID node - Customize two options in the C&R Tree node - List one difference between CHAID and C&R Tree

4. Using traditional statistical models - Explain key concepts for Linear - Customize options in the Linear node - Explain key concepts for Cox - Customize options in the Cox node

5: Using machine learning models - Explain key concepts for Neural Net - Customize one option in the Neural Net node

Prezzo di listino

700,00 EUR + IVA per partecipante

Durata

• 8 ore
• 1 giorno

Prerequisiti

• Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and a basic knowledge of modeling. - Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1.1) is recommended.