Feature matrix for email classification:
$begingroup$
This is in continuation of my earlier post.
In my previous model, I used just two features which couldn't fare well and gave 71% prediction (accuracy) score. Now, I'm trying to consider another important field "subject" from the data set. I'm planning to use NLTK package along with Tf-Idf.
I generated the below using NLTK (FreqDist) on the subject field -
Here is my idea:
I'll tokenize the subject field, apply stopwords, then Tf-Idf and get the top N most frequent words and use them as features. The Tf-Idf score will be my values. In total, I'll have (N+2) features.
But, one downside is - the feature matrix will be huge (say 200+ features) and sparse. Is that a concern? Does this design make sense?
Your thoughts please.
machine-learning multiclass-classification nltk
asked yesterday
ranit.branit.b
336
$endgroup$
add a comment |
$begingroup$
This is in continuation of my earlier post.
In my previous model, I used just two features which couldn't fare well and gave 71% prediction (accuracy) score. Now, I'm trying to consider another important field "subject" from the data set. I'm planning to use NLTK package along with Tf-Idf.
I generated the below using NLTK (FreqDist) on the subject field -
Here is my idea:
I'll tokenize the subject field, apply stopwords, then Tf-Idf and get the top N most frequent words and use them as features. The Tf-Idf score will be my values. In total, I'll have (N+2) features.
But, one downside is - the feature matrix will be huge (say 200+ features) and sparse. Is that a concern? Does this design make sense?
Your thoughts please.
machine-learning multiclass-classification nltk
asked yesterday
ranit.branit.b
336
$endgroup$
add a comment |
$begingroup$
This is in continuation of my earlier post.
In my previous model, I used just two features which couldn't fare well and gave 71% prediction (accuracy) score. Now, I'm trying to consider another important field "subject" from the data set. I'm planning to use NLTK package along with Tf-Idf.
I generated the below using NLTK (FreqDist) on the subject field -
Here is my idea:
I'll tokenize the subject field, apply stopwords, then Tf-Idf and get the top N most frequent words and use them as features. The Tf-Idf score will be my values. In total, I'll have (N+2) features.
But, one downside is - the feature matrix will be huge (say 200+ features) and sparse. Is that a concern? Does this design make sense?
Your thoughts please.
machine-learning multiclass-classification nltk
asked yesterday
ranit.branit.b
336
$endgroup$
This is in continuation of my earlier post.
In my previous model, I used just two features which couldn't fare well and gave 71% prediction (accuracy) score. Now, I'm trying to consider another important field "subject" from the data set. I'm planning to use NLTK package along with Tf-Idf.
I generated the below using NLTK (FreqDist) on the subject field -
Here is my idea:
I'll tokenize the subject field, apply stopwords, then Tf-Idf and get the top N most frequent words and use them as features. The Tf-Idf score will be my values. In total, I'll have (N+2) features.
But, one downside is - the feature matrix will be huge (say 200+ features) and sparse. Is that a concern? Does this design make sense?
Your thoughts please.
machine-learning multiclass-classification nltk
machine-learning multiclass-classification nltk
asked yesterday
ranit.branit.b
336
asked yesterday
ranit.branit.b
336
edited 12 hours ago
ranit.b
asked yesterday
ranit.branit.b
336
asked yesterday
ranit.branit.b
336
asked yesterday
ranit.branit.b
336
336
add a comment |
add a comment |
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