Please contact

Decision Trees give the possibility to make easy-to-understand displays of classification rules in a graphical way. The tree itself is a effective tool for making decisions. In the course you will learn to read and interpret Decision Trees, and to construct and optimize trees for your own by using available data. In practical exercises you will use the software CART, which uses the equal-named ground-breaking algorithm.

---

What would you learn ?
Does your production face big quality problems without ever having identified the reason?  Or do you work more in the trading domain and lose customers without knowing why ? If you have a huge amount of unused Data available, CART-Decision-Trees could very helpful to you. Their power allows you to understand the influence of certain parameters (temperature, duration, or customer age, sex) on your system. So you can optimize efficiently. In this course you will learn the basic concepts of classification- and regressiontrees, which will be explained by using pracitcal examples. Numerous excercises on the PC enable you to understand, how the presentated methods could help you the come to the right decision. In addition, you will get to know the user-friendly software CART, which many companies have used to improve their efficiency.

---

Who should attend ?

• Scientists, engineers and quality managers in Development, Production or Quality Assurance, which want to use huge data sets
• Marketing specialists, who want to analyse customer data in order to develop and support products well directed
• Elementary statistical knowledge is assumed (as given in the course "Visualization of Lab Data" )

---

Which topics are covered ?

Introduction
	Nonparametric data analysis Automating data analysis How to read a Decision Tree ? Practical examples Historic overview
Classification trees
	Dividing the population in subgroups,  division rules,  assignment of classes Growing and cutting of Decision Trees Visualization and interpretation of results Classification or regression ? Control parameters Practical examples
Regression trees
	Basics of regression Division rules
Practice
	Generate accurate files Use existing data Learn-, test- and validation data Your model: automatic generation of a Tree, selection of parameters, interpretation of results Automatic reporting, export of graphics Creating prognoses, export models for producti

---

Any questions ?

• Course duration: 2 days
  
• Participants: max. 12 (one PC per participant)
• Costs: including complete course documentation, coffee and lunch: see registration form
• Dates: see registration form
• Further information: see contact page