3b49ffd98696ad697cf2b9685e581459d51ea0b1,FeatureSelection.py,,,#,34
Before Change
//tf-idf
tfidfV = TfidfTransformer()
train_tfidf = tfidfV.fit_transform(train_count)
train_tfidf.shape
//get train data feature names
print(train_tfidf.A[:10])After Change
//we will start with simple bag of words technique
//creating feature vector - document term matrix
countV = CountVectorizer()
train_count = countV.fit_transform(DataPrep .train_news["Statement" ].values)
print(countV)
print(train_count)In pattern: SUPERPATTERN
Frequency: 3
Non-data size: 10
Instances Project Name: nishitpatel01/Fake_News_Detection
Commit Name: 3b49ffd98696ad697cf2b9685e581459d51ea0b1
Time: 2017-12-03
Author: nkp3@illinois.edu
File Name: FeatureSelection.py
Class Name:
Method Name:
Project Name: nishitpatel01/Fake_News_Detection
Commit Name: 99973b2eb7ee37508b81154154652ab282ea061d
Time: 2017-12-10
Author: nkp3@illinois.edu
File Name: classifier.py
Class Name:
Method Name:
Project Name: nishitpatel01/Fake_News_Detection
Commit Name: 95b8e2a603f66cc3091e3266a717c0f206be3e95
Time: 2017-12-03
Author: nkp3@illinois.edu
File Name: FeatureSelection.py
Class Name:
Method Name:
