def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
# return self.estimator.predict_proba(X)
decision = self.estimator.decision_function(X)
if len(self.estimator.classes_) > 2:
decision = _ovr_decision_function(decision < 0, decision,
len(self.estimator.classes_))
return softmax(decision)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
if self.loss in ["log", "modified_huber"]:
return self.estimator.predict_proba(X)
else:
df = self.estimator.decision_function(X)
return softmax(df)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
df = self.estimator.decision_function(X)
return softmax(df)