Computing TF-IDF on the whole dataset or only on training data?

人走茶凉 提交于 2020-06-13 18:45:45

问题


In the chapter seven of this book "TensorFlow Machine Learning Cookbook" the author in pre-processing data uses fit_transform function of scikit-learn to get the tfidf features of text for training. The author gives all text data to the function before separating it into train and test. Is it a true action or we must separate data first and then perform fit_transform on train and transform on test?


回答1:


I have not read the book and I am not sure whether this is actually a mistake in the book however I will give my 2 cents.

According to the documentation of scikit-learn, fit() is used in order to

Learn vocabulary and idf from training set.

On the other hand, fit_transform() is used in order to

Learn vocabulary and idf, return term-document matrix.

while transform()

Transforms documents to document-term matrix.

On the training set you need to apply both fit() and transform() (or just fit_transform() that essentially joins both operations) however, on the testing set you only need to transform() the testing instances (i.e. the documents).

Remember that training sets are used for learning purposes (learning is achieved through fit()) while testing set is used in order to evaluate whether the trained model can generalise well to new unseen data points.




回答2:


Author gives all text data before separating train and test to function. Is it a true action or we must separate data first then perform tfidf fit_transform on train and transform on test?

I would consider this as already leaking some information about the test set into the training set.

I tend to always follow the rule that before any pre-processing first thing to do is to separate the data, create a hold-out set.




回答3:


As we are talking about text data, we have to make sure that the model is trained only on the vocabulary of the training set as when we will deploy a model in real life, it will encounter words that it has never seen before so we have to do the validation on the test set keeping that in mind.
We have to make sure that the new words in the test set are not a part of the vocabulary of the model.
Hence we have to use fit_transform on the training data and transform on the test data. If you think about doing cross validation, then you can use this logic across all the folds.



来源:https://stackoverflow.com/questions/47778403/computing-tf-idf-on-the-whole-dataset-or-only-on-training-data

易学教程内所有资源均来自网络或用户发布的内容,如有违反法律规定的内容欢迎反馈
该文章没有解决你所遇到的问题?点击提问,说说你的问题,让更多的人一起探讨吧!