Here is some background on the problem. My aim is to classify text into some categories. I would like to get only good quality predictions from the model. If the model is not confident, I would like to classify the text manually.
Let's consider the example provided in http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html so that it's reproducible. In the following example, a classification model is trained and is fitted for test documents. One of the test document is - 'what the heck is this?'. I understand that model is returning the class which has highest probability. However, when the model is unsure, I would like to label the text as 'unable to classify'
from sklearn.naive_bayes import MultinomialNB clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target) docs_new = ['God is love', 'OpenGL on the GPU is fast', 'what the heck is this?'] X_new_counts = count_vect.transform(docs_new) X_new_tfidf = tfidf_transformer.transform(X_new_counts) predicted = clf.predict(X_new_tfidf) for doc, category in zip(docs_new, predicted): print('%r => %s' % (doc, twenty_train.target_names[category]))
'God is love' => soc.religion.christian 'OpenGL on the GPU is fast' => comp.graphics 'what the heck is this?' => soc.religion.christian
Predicting the probabilities
Here are the probabilities of prediction. Documents 1 and 2 have somewhat clear winner. However, the third document doesn't have one. I have about 100 classes and I would be hesitant to set a manual threshold.
clf.predict_proba(X_new_tfidf) array([[ 0.16297502, 0.03828016, 0.03737814, 0.76136668], [ 0.16387956, 0.36874738, 0.2364763 , 0.23089675], [ 0.28288106, 0.17035852, 0.2484853 , 0.29827513]])