[AI]/python.sklearn
sklearn.textdata.LogisticRegression적용
givemebro
2020. 4. 27. 17:31
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https://broscoding.tistory.com/203
sklearn.feature_extraction.text.CountVectorizer
BOW(Bag of words) : 단어집 만들기 from sklearn.feature_extraction.text import CountVectorizer ss=['I am Tom. Tom is me!','He is Tom. He is a man'] vect=CountVectorizer() vect.fit(ss) ''' CountVector..
broscoding.tistory.com
import numpy as np
# upload data file
imdb_tarin,imdb_test=np.load('imdb.npy')
# decode -> remove<br />
text_train=[s.decode().replace('<br />','') for s in imdb_tarin.data]
text_test=[s.decode().replace('<br .>','')for s in imdb_test.data]
y_train=imdb_tarin.target
y_test=imdb_test.target
from sklearn.feature_extraction.text import CountVectorizer
vect=CountVectorizer()
# train(train data)
vect.fit(text_train,y_train)
# define X_train
X_train=vect.transform(text_train)# sparse matrix
X_test=vect.transform(text_test)
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
# cross_val_score
scores=cross_val_score(LogisticRegression(C=1),X_test,y_test)
display(scores.mean())
# 0.8831598032887086
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