Naive bayes, all of the elements in predict_proba output matrix are less than 0.5
$begingroup$
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 multiclass-classification naive-bayes-classifier
asked 3 hours ago
hyTuevhyTuev
535
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add a comment |
$begingroup$
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 multiclass-classification naive-bayes-classifier
asked 3 hours ago
hyTuevhyTuev
535
$endgroup$
add a comment |
$begingroup$
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 multiclass-classification naive-bayes-classifier
asked 3 hours 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 multiclass-classification naive-bayes-classifier
machine-learning scikit-learn nlp multiclass-classification naive-bayes-classifier
asked 3 hours ago
hyTuevhyTuev
535
asked 3 hours ago
hyTuevhyTuev
535
edited 23 mins ago
hyTuev
asked 3 hours ago
hyTuevhyTuev
535
asked 3 hours ago
hyTuevhyTuev
535
asked 3 hours ago
hyTuevhyTuev
535
535
add a comment |
add a comment |
1 Answer
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No, your classifier can label text. It doesn't do it well but it is still almost 2 times better than random (for 7 classes, random will get you ~0.15 accuracy).
Looking at the test set is not enough. You need to create the same confusion matrix for you training set.
If the results you will get for the test set are similar in magnitude than maybe your model is too simple for the task or maybe you haven't trained it long enough.
If the results of the test set are good, than you might have a generalization problem (overfitting), which means that you need to increase the regularization during training. It also might mean that your training set comes from a different distribution than your test set.
answered 2 mins ago
Mark.FMark.F
589118
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1 Answer
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$begingroup$
No, your classifier can label text. It doesn't do it well but it is still almost 2 times better than random (for 7 classes, random will get you ~0.15 accuracy).
Looking at the test set is not enough. You need to create the same confusion matrix for you training set.
If the results you will get for the test set are similar in magnitude than maybe your model is too simple for the task or maybe you haven't trained it long enough.
If the results of the test set are good, than you might have a generalization problem (overfitting), which means that you need to increase the regularization during training. It also might mean that your training set comes from a different distribution than your test set.
answered 2 mins ago
Mark.FMark.F
589118
$endgroup$
add a comment |
$begingroup$
No, your classifier can label text. It doesn't do it well but it is still almost 2 times better than random (for 7 classes, random will get you ~0.15 accuracy).
Looking at the test set is not enough. You need to create the same confusion matrix for you training set.
If the results you will get for the test set are similar in magnitude than maybe your model is too simple for the task or maybe you haven't trained it long enough.
If the results of the test set are good, than you might have a generalization problem (overfitting), which means that you need to increase the regularization during training. It also might mean that your training set comes from a different distribution than your test set.
answered 2 mins ago
Mark.FMark.F
589118
$endgroup$
add a comment |
$begingroup$
No, your classifier can label text. It doesn't do it well but it is still almost 2 times better than random (for 7 classes, random will get you ~0.15 accuracy).
Looking at the test set is not enough. You need to create the same confusion matrix for you training set.
If the results you will get for the test set are similar in magnitude than maybe your model is too simple for the task or maybe you haven't trained it long enough.
If the results of the test set are good, than you might have a generalization problem (overfitting), which means that you need to increase the regularization during training. It also might mean that your training set comes from a different distribution than your test set.
answered 2 mins ago
Mark.FMark.F
589118
$endgroup$
No, your classifier can label text. It doesn't do it well but it is still almost 2 times better than random (for 7 classes, random will get you ~0.15 accuracy).
Looking at the test set is not enough. You need to create the same confusion matrix for you training set.
If the results you will get for the test set are similar in magnitude than maybe your model is too simple for the task or maybe you haven't trained it long enough.
If the results of the test set are good, than you might have a generalization problem (overfitting), which means that you need to increase the regularization during training. It also might mean that your training set comes from a different distribution than your test set.
answered 2 mins ago
Mark.FMark.F
589118
answered 2 mins ago
Mark.FMark.F
589118
answered 2 mins ago
Mark.FMark.F
589118
answered 2 mins ago
Mark.FMark.F
589118
589118
add a comment |
add a comment |
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