Neural Market Trends

Ensemble Modeling in RapidMiner

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Here’s a great 10 minute video by Vijay Kotu explaing the concept of ensembling modeling and how to do it in RapidMiner.

My notes are below:

  • Friend Example to explain Error Rates
  • P(Friend’s Circle wrong) = P(2 Friends wrong) + P(3 Friends wrong)
  • Use Binominal Distribution for each friend
  • Individual friends have a higher error rate than a group of friends
  • Diversity works and the math works!
  • One strong model can be created from multiple models
  • Individual models SHOULD be independent and classifier models should be better than 50% in error rate on their own.
  • Change/sample training data for each base model
  • Use different models
  • Change the parameters of the model (depth of trees, k value, etc)
  • Example process in RapidMiner uses Iris data set
  • Use a Vote operator to create a ‘stacked model’ using a majority vote
  • Bagging/Boosting Ensemble operators manipulate the rows of training data

Updated with 5 Minutes with Ingo

  • Ingo goes over Bagging and Boosting Ensemble methods
  • Ingo needs to lay off the sugar, LOL

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