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Mastering and Tuning Decision Trees
Rating: 3.6 out of 5(39 ratings)
383 students

Mastering and Tuning Decision Trees

IBM SPSS Modeler Seminar Series
Created bySandy Midili
Last updated 7/2014
English

What you'll learn

  • Understand the theory behind classification trees
  • Differentiate between classification tree algorithms
  • Know the assumptions of classification trees
  • Learn the advantage and disadvantages of the different algorithms
  • Interpret the results

Course content

2 sections • 24 lectures • 3h 3m total length

  • Characteristics of Tree Models2:51
  • Supervised Segmentation in Modeler2:59

    Analyze how supervised segmentation uses targets to differentiate churn among loyalty program customers, and compare decision trees with rule-based models using the auto classifier and expert tab.

  • Trees and Rules2:30
  • Defining Terms2:49
  • Additional Uses of Trees2:38
  • A First Look at the Data Set2:50
  • CHAID4:57
  • How does the CHAID algorithm work36:58
  • Using Autoclassifer to run multiple CHAID settings4:41
  • How does the C&RT algorithm work?12:31
  • Surrogates for Missing Data12:32
  • C&RT's Expert Settings19:14
  • CHAID Expert Settings9:40
  • Audience Question: How do you compare the results of two models?2:14
  • Should We Adjust the Models12:25
  • What is Bagging and Boosting?7:50

Requirements

  • Knowledge or experience with IBM SPSS Modeler or completion of an introductory level data mining course and on the job data mining experience.

Description

IBM SPSS Modeler is a data mining workbench that allows you to build predictive models quickly and intuitively without programming. Analysts typically use SPSS Modeler to analyze data by mining historical data and then deploying models to generate predictions for recent (or even real-time) data.

Overview: Mastering and Tuning Decision Trees is a series of self-paced videos that discusses the decision tree methods (CHAID, C5.0, CRT, and QUEST) available in IBM SPSS Modeler. These techniques produces a rule based predictive model for an outcome variable based on the values of the predictor variables. Students will gain an understanding of the situations in which one would this technique, its assumptions, how to do the analysis automatically as well as interactively, and how to interpret the results. Particular emphasis is made on contrasting CHAID and C&RT in detail. Tuning – the adjusting of parameters to optimize performance – is demonstrated using both CHAID and C&RT.

Who this course is for:

  • Anyone that has experience with IBM SPSS Modeler or has completed an introductory level data mining course and would like to learn about decision tree models.

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