FWIW, I had some luck with this modification of AdaBoostClassifier:
diff --git a/sklearn/ensemble/weight_boosting.py b/sklearn/ensemble/weight_boost
index 46467d7..bc5a762 100644
--- a/sklearn/ensemble/weight_boosting.py
+++ b/sklearn/ensemble/weight_boosting.py
@@ -573,6 +573,9 @@ class AdaBoostClassifier(BaseWeightBoosting, ClassifierMixin
np.log((1. - estimator_error) / estimator_error) +
np.log(n_classes - 1.))
+ # Boost false-positives... bit of a hack.
+ incorrect = 2*(y_predict > y) + 1*(y_predict < y)
+
# Only boost the weights if I will fit again
if not iboost == self.n_estimators - 1:
# Only boost positive weights
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