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 sample, I want to calculate the probability for each of the target labels. So my prediction would consist of 7 probabilities for each row.
On the sklearn website I read about multi-label classification, but this doesn't seem to be what I want.
I tried the following code, but this only gives me one classification per sample.
from sklearn.multiclass import OneVsRestClassifier
clf = OneVsRestClassifier(DecisionTreeClassifier())
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
Does anyone have some advice on this? Thanks!
You can do that by simply removing the OneVsRestClassifer
and using predict_proba
method of the DecisionTreeClassifier
. You can do the following:
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
pred = clf.predict_proba(X_test)
This will give you a probability for each of your 7 possible classes.
Hope that helps!