问题
I am currently trying to build a text classification model (document classification) with roughly 80 classes. When I build and train the model using random forest (after vectorizing the text into a TF-IDF matrix), the model works well. However, when I introduce new data, the same words that I used to build my RF aren't necessarily identical to the training set. This is a problem because I have a different number of features in my training set than I do in my test set (so the dimensions for the training set are less than the test).
####### Convert bag of words to TFIDF matrix
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(data)
print tfidf_matrix.shape
## number of features = 421
####### Train Random Forest Model
clf = RandomForestClassifier(max_depth=None,min_samples_split=1, random_state=1,n_jobs=-1)
####### k-fold cross validation
scores = cross_val_score(clf, tfidf_matrix.toarray(),labels,cv=7,n_jobs=-1)
print scores.mean()
### this is the new data matrix for unseen data
new_tfidf = tfidf_vectorizer.fit_transform(new_X)
### number of features = 619
clf.fit(tfidf_matrix.toarray(),labels)
clf.predict(new_tfidf.toarray())
How can I go about creating a working RF model for classification that will incorporate new features (words) that weren't seen in the training?
回答1:
Do not call fit_transform on the unseen data, only transform! That will keep the dictionary from the training set.
回答2:
You cannot introduce new features into the test set that were not part of your training set. The model is trained on a specific dictionary of terms and that same dictionary of terms must be used across training, validating, testing, and production. Further more, the indices of the words in your feature vector cannot change either.
You should be creating one large matrix using all of your data and then split the rows into your train and test sets. This will guarantee that you will have the same feature set for train and test.
来源：https://stackoverflow.com/questions/24806383/python-vectorization-for-classification