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Data Mining with CART Decision Trees

Mon/Tue 8-9/6/2020

Disculpe las molestias, la página solicitada no está disponible en español.

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 ?
 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
 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 ?
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