All of the elements in predict_proba output matrix are less than 0.5
0
$begingroup$
I've created a MultinomialNB classifier model by which I'm trying to label some test texts:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import preprocessing
from sklearn.naive_bayes import MultinomialNB
tfv = TfidfVectorizer(strip_accents='unicode', analyzer='word',token_pattern=r'w{1,}',
use_idf=1,smooth_idf=1,sublinear_tf=1)
# df['text'] is a long string text of words
tfv.fit(df['text'])
lbl_enc = preprocessing.LabelEncoder()
# df['which_subject'] is one of the following 7 subjects: ['Educational', 'Political', 'Sports', 'Tech', 'Social', 'Religions', 'Economics']
y = lbl_enc.fit_transform(df['which_subject'])
xtrain_tfv = tfv.transform(df['text'])
# xtest_tfv has 7 samples
xtest_tfv = tfv.transform(test_df['text'])
clf = MultinomialNB()
clf.fit(xtrain_tfv, y)
y_test_preds = clf.predict_proba(xtest_tfv)
Now y_test_preds is as follows:
0.255328 0.118111 0.129958 0.123368 0.119301 0.131098 0.122836
0.122814 0.265444 0.117637 0.13531 0.116697 0.122812 0.119286
0.131485 0.114459 0.258224 0.122414 0.118132 0.134005 0.12128
0.125075 0.131948 0.122668 0.258655 0.116518 0.119995 0.12514
0.124356 0.116987 0.121706 0.119796 0.266172 0.127231 0.123751
0.132295 0.1192 0.13366 0.119445 0.123186 0.257318 0.114895
0.126779 0.118406 0.123723 0.127393 0.122539 0.117509 0.263652
As you see, all of the elements are less than 0.5. Does this table show anything? Can I conclude that the classifier is not able to label test text?
machine-learning scikit-learn nlp text-mining multiclass-classification
asked 1 hour ago
hyTuevhyTuev
535
$endgroup$
add a comment |
0
$begingroup$
I've created a MultinomialNB classifier model by which I'm trying to label some test texts:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import preprocessing
from sklearn.naive_bayes import MultinomialNB
tfv = TfidfVectorizer(strip_accents='unicode', analyzer='word',token_pattern=r'w{1,}',
use_idf=1,smooth_idf=1,sublinear_tf=1)
# df['text'] is a long string text of words
tfv.fit(df['text'])
lbl_enc = preprocessing.LabelEncoder()
# df['which_subject'] is one of the following 7 subjects: ['Educational', 'Political', 'Sports', 'Tech', 'Social', 'Religions', 'Economics']
y = lbl_enc.fit_transform(df['which_subject'])
xtrain_tfv = tfv.transform(df['text'])
# xtest_tfv has 7 samples
xtest_tfv = tfv.transform(test_df['text'])
clf = MultinomialNB()
clf.fit(xtrain_tfv, y)
y_test_preds = clf.predict_proba(xtest_tfv)
Now y_test_preds is as follows:
0.255328 0.118111 0.129958 0.123368 0.119301 0.131098 0.122836
0.122814 0.265444 0.117637 0.13531 0.116697 0.122812 0.119286
0.131485 0.114459 0.258224 0.122414 0.118132 0.134005 0.12128
0.125075 0.131948 0.122668 0.258655 0.116518 0.119995 0.12514
0.124356 0.116987 0.121706 0.119796 0.266172 0.127231 0.123751
0.132295 0.1192 0.13366 0.119445 0.123186 0.257318 0.114895
0.126779 0.118406 0.123723 0.127393 0.122539 0.117509 0.263652
As you see, all of the elements are less than 0.5. Does this table show anything? Can I conclude that the classifier is not able to label test text?
machine-learning scikit-learn nlp text-mining multiclass-classification
asked 1 hour ago
hyTuevhyTuev
535
$endgroup$
add a comment |
0
0
0
$begingroup$
I've created a MultinomialNB classifier model by which I'm trying to label some test texts:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import preprocessing
from sklearn.naive_bayes import MultinomialNB
tfv = TfidfVectorizer(strip_accents='unicode', analyzer='word',token_pattern=r'w{1,}',
use_idf=1,smooth_idf=1,sublinear_tf=1)
# df['text'] is a long string text of words
tfv.fit(df['text'])
lbl_enc = preprocessing.LabelEncoder()
# df['which_subject'] is one of the following 7 subjects: ['Educational', 'Political', 'Sports', 'Tech', 'Social', 'Religions', 'Economics']
y = lbl_enc.fit_transform(df['which_subject'])
xtrain_tfv = tfv.transform(df['text'])
# xtest_tfv has 7 samples
xtest_tfv = tfv.transform(test_df['text'])
clf = MultinomialNB()
clf.fit(xtrain_tfv, y)
y_test_preds = clf.predict_proba(xtest_tfv)
Now y_test_preds is as follows:
0.255328 0.118111 0.129958 0.123368 0.119301 0.131098 0.122836
0.122814 0.265444 0.117637 0.13531 0.116697 0.122812 0.119286
0.131485 0.114459 0.258224 0.122414 0.118132 0.134005 0.12128
0.125075 0.131948 0.122668 0.258655 0.116518 0.119995 0.12514
0.124356 0.116987 0.121706 0.119796 0.266172 0.127231 0.123751
0.132295 0.1192 0.13366 0.119445 0.123186 0.257318 0.114895
0.126779 0.118406 0.123723 0.127393 0.122539 0.117509 0.263652
As you see, all of the elements are less than 0.5. Does this table show anything? Can I conclude that the classifier is not able to label test text?
machine-learning scikit-learn nlp text-mining multiclass-classification
asked 1 hour ago
hyTuevhyTuev
535
$endgroup$
I've created a MultinomialNB classifier model by which I'm trying to label some test texts:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import preprocessing
from sklearn.naive_bayes import MultinomialNB
tfv = TfidfVectorizer(strip_accents='unicode', analyzer='word',token_pattern=r'w{1,}',
use_idf=1,smooth_idf=1,sublinear_tf=1)
# df['text'] is a long string text of words
tfv.fit(df['text'])
lbl_enc = preprocessing.LabelEncoder()
# df['which_subject'] is one of the following 7 subjects: ['Educational', 'Political', 'Sports', 'Tech', 'Social', 'Religions', 'Economics']
y = lbl_enc.fit_transform(df['which_subject'])
xtrain_tfv = tfv.transform(df['text'])
# xtest_tfv has 7 samples
xtest_tfv = tfv.transform(test_df['text'])
clf = MultinomialNB()
clf.fit(xtrain_tfv, y)
y_test_preds = clf.predict_proba(xtest_tfv)
Now y_test_preds is as follows:
0.255328 0.118111 0.129958 0.123368 0.119301 0.131098 0.122836
0.122814 0.265444 0.117637 0.13531 0.116697 0.122812 0.119286
0.131485 0.114459 0.258224 0.122414 0.118132 0.134005 0.12128
0.125075 0.131948 0.122668 0.258655 0.116518 0.119995 0.12514
0.124356 0.116987 0.121706 0.119796 0.266172 0.127231 0.123751
0.132295 0.1192 0.13366 0.119445 0.123186 0.257318 0.114895
0.126779 0.118406 0.123723 0.127393 0.122539 0.117509 0.263652
As you see, all of the elements are less than 0.5. Does this table show anything? Can I conclude that the classifier is not able to label test text?
machine-learning scikit-learn nlp text-mining multiclass-classification
machine-learning scikit-learn nlp text-mining multiclass-classification
asked 1 hour ago
hyTuevhyTuev
535
asked 1 hour ago
hyTuevhyTuev
535
edited 1 hour ago
hyTuev
asked 1 hour ago
hyTuevhyTuev
535
asked 1 hour ago
hyTuevhyTuev
535
asked 1 hour ago
hyTuevhyTuev
535
535
add a comment |
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