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 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 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 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
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
- 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.
Prezzo di listino: 700,00 EUR + IVA per partecipante
Durata: 8 ore
Prossime edizioni
Milano
13 luglio
Prezzo: 700,00 EUR + IVA
Milano
11 ottobre
Prezzo: 700,00 EUR + IVA
Erogabile on-line e on-site
Tutti i nostri corsi sono erogabili anche in modalitĂ on-line (con formazione a distanza), oppure on-site, sempre personalizzati secondo le esigenze.
