decision tree - Machine Learning - AdaBoost the best standalone algorithm -


on given problem, i've checked using k-fold (k=10) few standalone algorithms among them cart , knn.

knn came upper hand on cart , furthermore tuned using best k found (3).

when went on check ensemble methods tried adaboost tuned knn , default cart , surprisingly cart performed better.

i'm not deep math behind scenes don't understand how makes sense.

so 2 questions:

  1. how come adaboost cart performed better tuned knn?
  2. if standalone performance not predict ensemble base estimator, how wisely choose base estimators ensemble methods?


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