AdaBoostM1
Relation: diebold
Correctly Classified Instances 204 88.3117 %
Incorrectly Classified Instances 27 11.6883 %
Kappa statistic 0.7647
Mean absolute error 0.1565
Root mean squared error 0.3046
Relative absolute error 31.5858 % ...
Algorithm: Adaboost. A boosting algorithm-create an ensemble of classifiers. Each one gives a weighted vote.
If a convex loss is utilized (as in AdaBoost, LogitBoost, and all members of the AnyBoost family of algorithms) then an example with higher margin will receive less (or equal) weight than an example with lower margin.
"Formally, it is based on confidence-rated boosting, a variant of AdaBoost developed by Rob Schapire and Yoram Singer in 1999. The code is based on William Cohen's widely-used RIPPER learning system. ...
 See also: Boosting, Machine learning, Generalization, Classification, Distribution
  
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