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bow_vectorizer = CountVectorizer(min_df=0.001)

bow_vectorizer = CountVectorizer(min_df=0.001)

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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bow_vectorizer = CountVectorizer(min_df=0.001)
# your code here

 

 

 

### TEST bag-of-words vectorization
print(textfeats_tr.shape)
print(textfeats_te.shape)

assert textfeats_tr.shape[1] == textfeats_te.shape[1]
assert textfeats_tr.shape[1] == 2468
assert len(bow_vectorizer.vocabulary_) == textfeats_tr.shape[1]
assert len(bow_vectorizer.stop_words_) > 10000

Perform the Bag-of-Words vectorization of the texts in description column, train the linear regression model on the obtained numerical features and evaluate its mean absolute error on the test set. • Use CountVectorizer from sklearn to perform Bag-of-Words vectorization. · Use the argument min_df=0.001 to remove the words which appear in less then 0.1% of the documents · Read more about it the documentation • Fit the vectorizer using descriptions from the train dataset • Create textfeats_tr which contains transformed descriptions from the train dataset • Create textfeats_te which contains transformed descriptions from the test dataset
Transcribed Image Text:Perform the Bag-of-Words vectorization of the texts in description column, train the linear regression model on the obtained numerical features and evaluate its mean absolute error on the test set. • Use CountVectorizer from sklearn to perform Bag-of-Words vectorization. · Use the argument min_df=0.001 to remove the words which appear in less then 0.1% of the documents · Read more about it the documentation • Fit the vectorizer using descriptions from the train dataset • Create textfeats_tr which contains transformed descriptions from the train dataset • Create textfeats_te which contains transformed descriptions from the test dataset
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