多标签文本分类的分类报告？

Question

I'm working on multilabel text classification. I'm tried to print the classification report for the machine learning but its print for each class alone. how I can get the classification report for all classes together? This part of the code

this code for the labels

categories = list(data_raw.columns.values)
categories = categories[1:]

The Evaluation:

def modelEvaluation(predictions, y_test_set):
    print("\nAccuracy on validation set: {:.4f}".format(accuracy_score(y_test_set, predictions)))
    print("\nClassification report : \n", metrics.classification_report(y_test_set, predictions))
    print("\nConfusion Matrix : \n", multilabel_confusion_matrix(y_test_set, predictions))

and this for ML

from sklearn.svm import LinearSVC


SVC_pipeline = Pipeline([
                    ('clf', OneVsRestClassifier(LinearSVC(), n_jobs=1)),
            ])


for category in categories:
    printmd('**Processing {} comments...**'.format(category))
    
    # Training logistic regression model on train data
    SVC_pipeline.fit(x_train, train[category])
    
    # calculating test accuracy
    prediction = SVC_pipeline.predict(x_test)
    print('Test accuracy is {}'.format(accuracy_score(test[category], prediction)))
    print("\n")
    
    modelEvaluation(prediction, test[category])

if I tried to print the classification report alone like the below code, it gives me the result for the last class

from sklearn.metrics import classification_report
print("\nClassification report : \n", metrics.classification_report(test[category], prediction))

Answer 1

Use without test[category] and provide the whole test set which contains all classes that you build your model for.

print("\nClassification report : \n", metrics.classification_report(y_test, predictions))

Where y_test is ground truth labels (True outputs) for test set X_test .

You are passing test set ( X_test ) instead of labels ( y_test ) for that test set.