pybdt User Manual

This is the user manual for pybdt.

• Statement of the problem
• The ABC example
  • Input variable distributions
• Introduction to decision tree classifiers
  • Classification example
  • Some definitions
  • Scoring using purity information
  • Decision tree training
    • Quantifying cut effectiveness
    • Stopping criteria
• Introduction to boosted decision tree classifiers
  • AdaBoost
  • Calculating the BDT score
  • Example BDT score distribution
• Introduction to overtraining
  • Training sample overtraining
  • Data/Monte Carlo overtraining
• Introduction to pruning
  • Same leaf pruning
  • Cost complexity pruning
• Introduction to decision tree randomization
• DataSets and Training
  • DataSet objects
  • Training
• Validator Setup
  • Validator Construction
  • DataSet weighting
• Validator Usage
  • Summary Plots
  • Overtraining Check
  • Other Plotting Features
• Tree and Forest Inspection
  • Direct Acces
  • Visualization
  • Variable Importance
• Classifier Application
  • Scoring with IceTray
  • Scoring without IceTray
• Global Epsilon Reference