How do I use TF*IDF scores for my machine learning model? The Next CEO of Stack Overflow2019 Community Moderator ElectionDocument classification: tf-idf prior to or after feature filtering?Choosing an algorithm with normalized data(Classification)Image Feature VectorsHow to improve the accuracy of Random Forest for Text CategorizationHow to find categorical features from a vector representation of text?Low number of inputs compared to outputs (per row) in neural networkFeeding machine learning model with different matrixWhy Decision trees performs better than logistic regressionWhen to use an ordinal logistic regression modelHow exactly do I go about extracting features from timestamps for machine learning?
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How do I use TF*IDF scores for my machine learning model?
The Next CEO of Stack Overflow2019 Community Moderator ElectionDocument classification: tf-idf prior to or after feature filtering?Choosing an algorithm with normalized data(Classification)Image Feature VectorsHow to improve the accuracy of Random Forest for Text CategorizationHow to find categorical features from a vector representation of text?Low number of inputs compared to outputs (per row) in neural networkFeeding machine learning model with different matrixWhy Decision trees performs better than logistic regressionWhen to use an ordinal logistic regression modelHow exactly do I go about extracting features from timestamps for machine learning?
$begingroup$
I have applied TF*IDF on the 'Ad-topic line' column of my dataset.
For every ad-topic line, I get the same output:
Firstly, I am unable to make sense of the output. The TF*IDF values are mentioned to the right, but what exactly are the numbers in brackets?
I plan to use these for my logistic regression model for classification. How exactly do I feed these values to the algorithm?
machine-learning feature-extraction tfidf
asked Mar 23 at 21:18
ApolloApollo
61
New contributor
Apollo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have applied TF*IDF on the 'Ad-topic line' column of my dataset.
For every ad-topic line, I get the same output:
Firstly, I am unable to make sense of the output. The TF*IDF values are mentioned to the right, but what exactly are the numbers in brackets?
I plan to use these for my logistic regression model for classification. How exactly do I feed these values to the algorithm?
machine-learning feature-extraction tfidf
asked Mar 23 at 21:18
ApolloApollo
61
New contributor
Apollo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have applied TF*IDF on the 'Ad-topic line' column of my dataset.
For every ad-topic line, I get the same output:
Firstly, I am unable to make sense of the output. The TF*IDF values are mentioned to the right, but what exactly are the numbers in brackets?
I plan to use these for my logistic regression model for classification. How exactly do I feed these values to the algorithm?
machine-learning feature-extraction tfidf
asked Mar 23 at 21:18
ApolloApollo
61
New contributor
Apollo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have applied TF*IDF on the 'Ad-topic line' column of my dataset.
For every ad-topic line, I get the same output:
Firstly, I am unable to make sense of the output. The TF*IDF values are mentioned to the right, but what exactly are the numbers in brackets?
I plan to use these for my logistic regression model for classification. How exactly do I feed these values to the algorithm?
machine-learning feature-extraction tfidf
machine-learning feature-extraction tfidf
asked Mar 23 at 21:18
ApolloApollo
61
New contributor
Apollo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 23 at 21:18
ApolloApollo
61
New contributor
Apollo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 23 at 21:18
ApolloApollo
61
New contributor
Apollo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 23 at 21:18
ApolloApollo
61
asked Mar 23 at 21:18
ApolloApollo
61
61
New contributor
Apollo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Apollo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Apollo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
$begingroup$
The numbers on the left are also important, they are basically the indexes in the following format
(document_number, token_number)
TF-IDF is computed for all the unique tokens in all the documents.
Let me know if you have any further doubt.
Vote me if i was able to help ;)
answered Mar 24 at 0:16
William ScottWilliam Scott
1063
New contributor
William Scott is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
First of all, I'm not sure how you have applied tfidf vectorizers on the data as no code snippet has been attached. Tfidf vectorizers are applied on text to convert the text into numerical vectors. Speciality of tfidf vectorization is that it gives more importance to rarely occuring words than the words which occur a lot of time ex: stop words or filler words which occur a lot of times yet they add no special meaning to a sentence. Nevertheless, I've applied tfidf vectorization on the dataset and I have posted them here and here. Try to recreate this. Hope it helps
answered Mar 24 at 10:51
karthikeyan mgkarthikeyan mg
19510
$endgroup$
$begingroup$
I did the exact same thing. However, all tfidf values for each ad-topic line turn out to be the same. Please have a look at these: (1) Before fit_transfrom: imgur.com/NdOlwQL (2) Code: imgur.com/zuhGpJe (3) After fit_transform: imgur.com/YfFzIQr
$endgroup$
– Apollo
Mar 24 at 11:50
add a comment |
$begingroup$
From the output you have shared it can be understood that you have around 1000 rows of data and 300+ features/columns/words which the tfidf function created based on your selection of ngrams parameter.
Now, in the brackets (x,y) signifies:
X as the number of row of your data
And
Y as the nth feature in the features list.
Assuming you have written something similar to get the above output-
tfidf_matrix = tf.fit_transform(data)
this will give you a list of feature names
feature_names = tf.get_feature_names()
And now you can check any Y value in the brackets, for example lets take the first value from your output- (0,53)
feature_names[53]
This will give the name of the feature(which is basically a word or a combination of words) and right side value is the the tfidf score of that feature in the 0th row of your data.
answered Mar 24 at 5:58
Cini09Cini09
166
$endgroup$
$begingroup$
Thank you for the response. Could you please tell me why every ad-topic line gives the same output? Shouldn't the 0th row give me (0,53), (0,4) and (0,228) with their respective tfidf values? Instead, for every row, I get the same tfidf value list for all the rows(the image in the question)
$endgroup$
– Apollo
Mar 24 at 8:25
$begingroup$
for (0,53) its .61, (0,4) its .53 and (0,228) its .58
$endgroup$
– Cini09
Mar 25 at 18:59
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
The numbers on the left are also important, they are basically the indexes in the following format
(document_number, token_number)
TF-IDF is computed for all the unique tokens in all the documents.
Let me know if you have any further doubt.
Vote me if i was able to help ;)
answered Mar 24 at 0:16
William ScottWilliam Scott
1063
New contributor
William Scott is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
The numbers on the left are also important, they are basically the indexes in the following format
(document_number, token_number)
TF-IDF is computed for all the unique tokens in all the documents.
Let me know if you have any further doubt.
Vote me if i was able to help ;)
answered Mar 24 at 0:16
William ScottWilliam Scott
1063
New contributor
William Scott is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
The numbers on the left are also important, they are basically the indexes in the following format
(document_number, token_number)
TF-IDF is computed for all the unique tokens in all the documents.
Let me know if you have any further doubt.
Vote me if i was able to help ;)
answered Mar 24 at 0:16
William ScottWilliam Scott
1063
New contributor
William Scott is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
The numbers on the left are also important, they are basically the indexes in the following format
(document_number, token_number)
TF-IDF is computed for all the unique tokens in all the documents.
Let me know if you have any further doubt.
Vote me if i was able to help ;)
answered Mar 24 at 0:16
William ScottWilliam Scott
1063
New contributor
William Scott is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered Mar 24 at 0:16
William ScottWilliam Scott
1063
New contributor
William Scott is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered Mar 24 at 0:16
William ScottWilliam Scott
1063
answered Mar 24 at 0:16
William ScottWilliam Scott
1063
1063
New contributor
William Scott is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
William Scott is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
William Scott is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
$begingroup$
First of all, I'm not sure how you have applied tfidf vectorizers on the data as no code snippet has been attached. Tfidf vectorizers are applied on text to convert the text into numerical vectors. Speciality of tfidf vectorization is that it gives more importance to rarely occuring words than the words which occur a lot of time ex: stop words or filler words which occur a lot of times yet they add no special meaning to a sentence. Nevertheless, I've applied tfidf vectorization on the dataset and I have posted them here and here. Try to recreate this. Hope it helps
answered Mar 24 at 10:51
karthikeyan mgkarthikeyan mg
19510
$endgroup$
$begingroup$
I did the exact same thing. However, all tfidf values for each ad-topic line turn out to be the same. Please have a look at these: (1) Before fit_transfrom: imgur.com/NdOlwQL (2) Code: imgur.com/zuhGpJe (3) After fit_transform: imgur.com/YfFzIQr
$endgroup$
– Apollo
Mar 24 at 11:50
add a comment |
$begingroup$
First of all, I'm not sure how you have applied tfidf vectorizers on the data as no code snippet has been attached. Tfidf vectorizers are applied on text to convert the text into numerical vectors. Speciality of tfidf vectorization is that it gives more importance to rarely occuring words than the words which occur a lot of time ex: stop words or filler words which occur a lot of times yet they add no special meaning to a sentence. Nevertheless, I've applied tfidf vectorization on the dataset and I have posted them here and here. Try to recreate this. Hope it helps
answered Mar 24 at 10:51
karthikeyan mgkarthikeyan mg
19510
$endgroup$
$begingroup$
I did the exact same thing. However, all tfidf values for each ad-topic line turn out to be the same. Please have a look at these: (1) Before fit_transfrom: imgur.com/NdOlwQL (2) Code: imgur.com/zuhGpJe (3) After fit_transform: imgur.com/YfFzIQr
$endgroup$
– Apollo
Mar 24 at 11:50
add a comment |
$begingroup$
First of all, I'm not sure how you have applied tfidf vectorizers on the data as no code snippet has been attached. Tfidf vectorizers are applied on text to convert the text into numerical vectors. Speciality of tfidf vectorization is that it gives more importance to rarely occuring words than the words which occur a lot of time ex: stop words or filler words which occur a lot of times yet they add no special meaning to a sentence. Nevertheless, I've applied tfidf vectorization on the dataset and I have posted them here and here. Try to recreate this. Hope it helps
answered Mar 24 at 10:51
karthikeyan mgkarthikeyan mg
19510
$endgroup$
First of all, I'm not sure how you have applied tfidf vectorizers on the data as no code snippet has been attached. Tfidf vectorizers are applied on text to convert the text into numerical vectors. Speciality of tfidf vectorization is that it gives more importance to rarely occuring words than the words which occur a lot of time ex: stop words or filler words which occur a lot of times yet they add no special meaning to a sentence. Nevertheless, I've applied tfidf vectorization on the dataset and I have posted them here and here. Try to recreate this. Hope it helps
answered Mar 24 at 10:51
karthikeyan mgkarthikeyan mg
19510
answered Mar 24 at 10:51
karthikeyan mgkarthikeyan mg
19510
answered Mar 24 at 10:51
karthikeyan mgkarthikeyan mg
19510
answered Mar 24 at 10:51
karthikeyan mgkarthikeyan mg
19510
19510
$begingroup$
I did the exact same thing. However, all tfidf values for each ad-topic line turn out to be the same. Please have a look at these: (1) Before fit_transfrom: imgur.com/NdOlwQL (2) Code: imgur.com/zuhGpJe (3) After fit_transform: imgur.com/YfFzIQr
$endgroup$
– Apollo
Mar 24 at 11:50
add a comment |
$begingroup$
I did the exact same thing. However, all tfidf values for each ad-topic line turn out to be the same. Please have a look at these: (1) Before fit_transfrom: imgur.com/NdOlwQL (2) Code: imgur.com/zuhGpJe (3) After fit_transform: imgur.com/YfFzIQr
$endgroup$
– Apollo
Mar 24 at 11:50
$begingroup$
I did the exact same thing. However, all tfidf values for each ad-topic line turn out to be the same. Please have a look at these: (1) Before fit_transfrom: imgur.com/NdOlwQL (2) Code: imgur.com/zuhGpJe (3) After fit_transform: imgur.com/YfFzIQr
$endgroup$
– Apollo
Mar 24 at 11:50
$begingroup$
I did the exact same thing. However, all tfidf values for each ad-topic line turn out to be the same. Please have a look at these: (1) Before fit_transfrom: imgur.com/NdOlwQL (2) Code: imgur.com/zuhGpJe (3) After fit_transform: imgur.com/YfFzIQr
$endgroup$
– Apollo
Mar 24 at 11:50
add a comment |
$begingroup$
From the output you have shared it can be understood that you have around 1000 rows of data and 300+ features/columns/words which the tfidf function created based on your selection of ngrams parameter.
Now, in the brackets (x,y) signifies:
X as the number of row of your data
And
Y as the nth feature in the features list.
Assuming you have written something similar to get the above output-
tfidf_matrix = tf.fit_transform(data)
this will give you a list of feature names
feature_names = tf.get_feature_names()
And now you can check any Y value in the brackets, for example lets take the first value from your output- (0,53)
feature_names[53]
This will give the name of the feature(which is basically a word or a combination of words) and right side value is the the tfidf score of that feature in the 0th row of your data.
answered Mar 24 at 5:58
Cini09Cini09
166
$endgroup$
$begingroup$
Thank you for the response. Could you please tell me why every ad-topic line gives the same output? Shouldn't the 0th row give me (0,53), (0,4) and (0,228) with their respective tfidf values? Instead, for every row, I get the same tfidf value list for all the rows(the image in the question)
$endgroup$
– Apollo
Mar 24 at 8:25
$begingroup$
for (0,53) its .61, (0,4) its .53 and (0,228) its .58
$endgroup$
– Cini09
Mar 25 at 18:59
add a comment |
$begingroup$
From the output you have shared it can be understood that you have around 1000 rows of data and 300+ features/columns/words which the tfidf function created based on your selection of ngrams parameter.
Now, in the brackets (x,y) signifies:
X as the number of row of your data
And
Y as the nth feature in the features list.
Assuming you have written something similar to get the above output-
tfidf_matrix = tf.fit_transform(data)
this will give you a list of feature names
feature_names = tf.get_feature_names()
And now you can check any Y value in the brackets, for example lets take the first value from your output- (0,53)
feature_names[53]
This will give the name of the feature(which is basically a word or a combination of words) and right side value is the the tfidf score of that feature in the 0th row of your data.
answered Mar 24 at 5:58
Cini09Cini09
166
$endgroup$
$begingroup$
Thank you for the response. Could you please tell me why every ad-topic line gives the same output? Shouldn't the 0th row give me (0,53), (0,4) and (0,228) with their respective tfidf values? Instead, for every row, I get the same tfidf value list for all the rows(the image in the question)
$endgroup$
– Apollo
Mar 24 at 8:25
$begingroup$
for (0,53) its .61, (0,4) its .53 and (0,228) its .58
$endgroup$
– Cini09
Mar 25 at 18:59
add a comment |
$begingroup$
From the output you have shared it can be understood that you have around 1000 rows of data and 300+ features/columns/words which the tfidf function created based on your selection of ngrams parameter.
Now, in the brackets (x,y) signifies:
X as the number of row of your data
And
Y as the nth feature in the features list.
Assuming you have written something similar to get the above output-
tfidf_matrix = tf.fit_transform(data)
this will give you a list of feature names
feature_names = tf.get_feature_names()
And now you can check any Y value in the brackets, for example lets take the first value from your output- (0,53)
feature_names[53]
This will give the name of the feature(which is basically a word or a combination of words) and right side value is the the tfidf score of that feature in the 0th row of your data.
answered Mar 24 at 5:58
Cini09Cini09
166
$endgroup$
From the output you have shared it can be understood that you have around 1000 rows of data and 300+ features/columns/words which the tfidf function created based on your selection of ngrams parameter.
Now, in the brackets (x,y) signifies:
X as the number of row of your data
And
Y as the nth feature in the features list.
Assuming you have written something similar to get the above output-
tfidf_matrix = tf.fit_transform(data)
this will give you a list of feature names
feature_names = tf.get_feature_names()
And now you can check any Y value in the brackets, for example lets take the first value from your output- (0,53)
feature_names[53]
This will give the name of the feature(which is basically a word or a combination of words) and right side value is the the tfidf score of that feature in the 0th row of your data.
answered Mar 24 at 5:58
Cini09Cini09
166
edited Mar 25 at 18:57
answered Mar 24 at 5:58
Cini09Cini09
166
answered Mar 24 at 5:58
Cini09Cini09
166
answered Mar 24 at 5:58
Cini09Cini09
166
166
$begingroup$
Thank you for the response. Could you please tell me why every ad-topic line gives the same output? Shouldn't the 0th row give me (0,53), (0,4) and (0,228) with their respective tfidf values? Instead, for every row, I get the same tfidf value list for all the rows(the image in the question)
$endgroup$
– Apollo
Mar 24 at 8:25
$begingroup$
for (0,53) its .61, (0,4) its .53 and (0,228) its .58
$endgroup$
– Cini09
Mar 25 at 18:59
add a comment |
$begingroup$
Thank you for the response. Could you please tell me why every ad-topic line gives the same output? Shouldn't the 0th row give me (0,53), (0,4) and (0,228) with their respective tfidf values? Instead, for every row, I get the same tfidf value list for all the rows(the image in the question)
$endgroup$
– Apollo
Mar 24 at 8:25
$begingroup$
for (0,53) its .61, (0,4) its .53 and (0,228) its .58
$endgroup$
– Cini09
Mar 25 at 18:59
$begingroup$
Thank you for the response. Could you please tell me why every ad-topic line gives the same output? Shouldn't the 0th row give me (0,53), (0,4) and (0,228) with their respective tfidf values? Instead, for every row, I get the same tfidf value list for all the rows(the image in the question)
$endgroup$
– Apollo
Mar 24 at 8:25
$begingroup$
Thank you for the response. Could you please tell me why every ad-topic line gives the same output? Shouldn't the 0th row give me (0,53), (0,4) and (0,228) with their respective tfidf values? Instead, for every row, I get the same tfidf value list for all the rows(the image in the question)
$endgroup$
– Apollo
Mar 24 at 8:25
$begingroup$
for (0,53) its .61, (0,4) its .53 and (0,228) its .58
$endgroup$
– Cini09
Mar 25 at 18:59
$begingroup$
for (0,53) its .61, (0,4) its .53 and (0,228) its .58
$endgroup$
– Cini09
Mar 25 at 18:59
add a comment |
Apollo is a new contributor. Be nice, and check out our Code of Conduct.
Apollo is a new contributor. Be nice, and check out our Code of Conduct.
Apollo is a new contributor. Be nice, and check out our Code of Conduct.
Apollo is a new contributor. Be nice, and check out our Code of Conduct.
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