Probability of a data point being well classified
All these algorithms 
AdaBoostClassifier, BaggingClassifier, BayesianGaussianMixture, BernoulliNB, CalibratedClassifierCV, ComplementNB, DecisionTreeClassifier, ExtraTreeClassifier, ExtraTreesClassifier, GaussianMixture, GaussianNB, GaussianProcessClassifier,
GradientBoostingClassifier, KNeighborsClassifier, LabelPropagation, LabelSpreading, LinearDiscriminantAnalysis, LogisticRegression, LogisticRegressionCV, MLPClassifier, MultinomialNB, NuSVC, QuadraticDiscriminantAnalysis, RandomForestClassifier, SGDClassifier, SVC, _BinaryGaussianProcessClassifierLaplace, _ConstantPredictor
support a method called predict_proba(self, X), that doest precisely that.
I have a mix of labeled and unlabeled data, this last one I would like to classify it by using semi-supervised learning. Suppose I have already an algorithm that gives me the best accuracy at predicting the labels of the training subsample. I want to use that algorithm to predict the labels of the unlabeled subsample. In semi-supervised learning, the pseudo-labeled data is added to the labeled (training) one. I would like to select from the pseudo-labeled data only those points that the probability of being well classified is higher than, let's say, 0.8, and repeat the procedure till all the unlabeled data is pseudo-labeled with high probability.
How could I achieve this? Is there a code or built-in function that helps me to compute such a probability?