可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试):
问题:
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!
回答1:
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!
回答2:
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.
