Sklearn - How to predict probability for all target labels

Question

I have a data set with a target variable that can have 7 different labels. Each sample in my training set has only one label for the target variable.

For each sampl

Answer 1

You can try using scikit-multilearn - an extension of sklearn that handles multilabel classification. If your labels are not overly correlated you can train one classifier per label and get all predictions - try (after pip install scikit-multilearn):

from skmultilearn.problem_transform import BinaryRelevance    
classifier = BinaryRelevance(classifier = DecisionTreeClassifier())

# train
classifier.fit(X_train, y_train)

# predict
predictions = classifier.predict(X_test)

Predictions will contain a sparse matrix of size (n_samples, n_labels) in your case - n_labels = 7, each column contains prediction per label for all samples.

In case your labels are correlated you might need more sophisticated methods for multi-label classification.

Disclaimer: I'm the author of scikit-multilearn, feel free to ask more questions.