How to predict output of my naive bayes classifier applied on nlp(Restaurant Review) for a single external...
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I have a build my naive Bayes classifier model for nlp using bags of word. Now I want to predict output for a single external input
. How can I do it?please find this github link for correction thanks
https://github.com/Kundan8296/Machine-Learning/blob/master/NLP.ipynb
python nlp
edited Nov 12 at 7:59
ChaosPredictor
1,90911624
asked Nov 11 at 15:36
KUNDAN KUMAR Roy
12
add a comment |
up vote
0
down vote
favorite
I have a build my naive Bayes classifier model for nlp using bags of word. Now I want to predict output for a single external input
. How can I do it?please find this github link for correction thanks
https://github.com/Kundan8296/Machine-Learning/blob/master/NLP.ipynb
python nlp
edited Nov 12 at 7:59
ChaosPredictor
1,90911624
asked Nov 11 at 15:36
KUNDAN KUMAR Roy
12
thanks in advance.
– KUNDAN KUMAR Roy
Nov 11 at 15:38
You need to post the code here with inputs and what you have tried so we can reproduce that.
– Franco Piccolo
Nov 11 at 15:40
Welcome to stackoverflow! Please take the tour and read the help pages. Helpful may be "how to ask good questions" and this question checklist. Users here are way more ready to help if you provide minimal, complete, and verifiable example with some input and the desired output.
– Mikhail Stepanov
Nov 11 at 15:46
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I have a build my naive Bayes classifier model for nlp using bags of word. Now I want to predict output for a single external input
. How can I do it?please find this github link for correction thanks
https://github.com/Kundan8296/Machine-Learning/blob/master/NLP.ipynb
python nlp
edited Nov 12 at 7:59
ChaosPredictor
1,90911624
asked Nov 11 at 15:36
KUNDAN KUMAR Roy
12
I have a build my naive Bayes classifier model for nlp using bags of word. Now I want to predict output for a single external input
. How can I do it?please find this github link for correction thanks
https://github.com/Kundan8296/Machine-Learning/blob/master/NLP.ipynb
python nlp
python nlp
edited Nov 12 at 7:59
ChaosPredictor
1,90911624
asked Nov 11 at 15:36
KUNDAN KUMAR Roy
12
edited Nov 12 at 7:59
ChaosPredictor
1,90911624
asked Nov 11 at 15:36
KUNDAN KUMAR Roy
12
edited Nov 12 at 7:59
ChaosPredictor
1,90911624
edited Nov 12 at 7:59
ChaosPredictor
1,90911624
edited Nov 12 at 7:59
ChaosPredictor
1,90911624
1,90911624
asked Nov 11 at 15:36
KUNDAN KUMAR Roy
12
asked Nov 11 at 15:36
KUNDAN KUMAR Roy
12
asked Nov 11 at 15:36
KUNDAN KUMAR Roy
12
12
thanks in advance.
– KUNDAN KUMAR Roy
Nov 11 at 15:38
You need to post the code here with inputs and what you have tried so we can reproduce that.
– Franco Piccolo
Nov 11 at 15:40
Welcome to stackoverflow! Please take the tour and read the help pages. Helpful may be "how to ask good questions" and this question checklist. Users here are way more ready to help if you provide minimal, complete, and verifiable example with some input and the desired output.
– Mikhail Stepanov
Nov 11 at 15:46
add a comment |
thanks in advance.
– KUNDAN KUMAR Roy
Nov 11 at 15:38
You need to post the code here with inputs and what you have tried so we can reproduce that.
– Franco Piccolo
Nov 11 at 15:40
Welcome to stackoverflow! Please take the tour and read the help pages. Helpful may be "how to ask good questions" and this question checklist. Users here are way more ready to help if you provide minimal, complete, and verifiable example with some input and the desired output.
– Mikhail Stepanov
Nov 11 at 15:46
thanks in advance.
– KUNDAN KUMAR Roy
Nov 11 at 15:38
thanks in advance.
– KUNDAN KUMAR Roy
Nov 11 at 15:38
You need to post the code here with inputs and what you have tried so we can reproduce that.
– Franco Piccolo
Nov 11 at 15:40
You need to post the code here with inputs and what you have tried so we can reproduce that.
– Franco Piccolo
Nov 11 at 15:40
Welcome to stackoverflow! Please take the tour and read the help pages. Helpful may be "how to ask good questions" and this question checklist. Users here are way more ready to help if you provide minimal, complete, and verifiable example with some input and the desired output.
– Mikhail Stepanov
Nov 11 at 15:46
Welcome to stackoverflow! Please take the tour and read the help pages. Helpful may be "how to ask good questions" and this question checklist. Users here are way more ready to help if you provide minimal, complete, and verifiable example with some input and the desired output.
– Mikhail Stepanov
Nov 11 at 15:46
add a comment |
1 Answer
1
active
oldest
votes
up vote
0
down vote
You need to apply the same preprocessing steps that you applied on your training data, and use it as an input to the classifier. Make sure you don't use fit_transform() on the new data, use transform() only.
#Change this part in your preprocessing, so you can keep the original vectorizer.
vect = CountVectorizer(tokenizer=lambda doc: doc, lowercase=False)
bag_of_words = vect.fit_transform(corpus)
...
...
# Now when predicting, use this
new_data = ... # your new input
new_x = vect.transform(new_data)
y_pred = classifier.predict(new_x)
answered Nov 11 at 15:47
Dani G
427411
I have appliied the same code as given above but it generates error."AttributeError: transform not found".please help
– KUNDAN KUMAR Roy
Nov 12 at 2:28
@KUNDANKUMARRoy I added a change to your preprocessing
– Dani G
Nov 12 at 2:39
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
You need to apply the same preprocessing steps that you applied on your training data, and use it as an input to the classifier. Make sure you don't use fit_transform() on the new data, use transform() only.
#Change this part in your preprocessing, so you can keep the original vectorizer.
vect = CountVectorizer(tokenizer=lambda doc: doc, lowercase=False)
bag_of_words = vect.fit_transform(corpus)
...
...
# Now when predicting, use this
new_data = ... # your new input
new_x = vect.transform(new_data)
y_pred = classifier.predict(new_x)
answered Nov 11 at 15:47
Dani G
427411
I have appliied the same code as given above but it generates error."AttributeError: transform not found".please help
– KUNDAN KUMAR Roy
Nov 12 at 2:28
@KUNDANKUMARRoy I added a change to your preprocessing
– Dani G
Nov 12 at 2:39
add a comment |
up vote
0
down vote
You need to apply the same preprocessing steps that you applied on your training data, and use it as an input to the classifier. Make sure you don't use fit_transform() on the new data, use transform() only.
#Change this part in your preprocessing, so you can keep the original vectorizer.
vect = CountVectorizer(tokenizer=lambda doc: doc, lowercase=False)
bag_of_words = vect.fit_transform(corpus)
...
...
# Now when predicting, use this
new_data = ... # your new input
new_x = vect.transform(new_data)
y_pred = classifier.predict(new_x)
answered Nov 11 at 15:47
Dani G
427411
I have appliied the same code as given above but it generates error."AttributeError: transform not found".please help
– KUNDAN KUMAR Roy
Nov 12 at 2:28
@KUNDANKUMARRoy I added a change to your preprocessing
– Dani G
Nov 12 at 2:39
add a comment |
up vote
0
down vote
up vote
0
down vote
You need to apply the same preprocessing steps that you applied on your training data, and use it as an input to the classifier. Make sure you don't use fit_transform() on the new data, use transform() only.
#Change this part in your preprocessing, so you can keep the original vectorizer.
vect = CountVectorizer(tokenizer=lambda doc: doc, lowercase=False)
bag_of_words = vect.fit_transform(corpus)
...
...
# Now when predicting, use this
new_data = ... # your new input
new_x = vect.transform(new_data)
y_pred = classifier.predict(new_x)
answered Nov 11 at 15:47
Dani G
427411
You need to apply the same preprocessing steps that you applied on your training data, and use it as an input to the classifier. Make sure you don't use fit_transform() on the new data, use transform() only.
#Change this part in your preprocessing, so you can keep the original vectorizer.
vect = CountVectorizer(tokenizer=lambda doc: doc, lowercase=False)
bag_of_words = vect.fit_transform(corpus)
...
...
# Now when predicting, use this
new_data = ... # your new input
new_x = vect.transform(new_data)
y_pred = classifier.predict(new_x)
answered Nov 11 at 15:47
Dani G
427411
edited Nov 12 at 13:25
answered Nov 11 at 15:47
Dani G
427411
answered Nov 11 at 15:47
Dani G
427411
answered Nov 11 at 15:47
Dani G
427411
427411
I have appliied the same code as given above but it generates error."AttributeError: transform not found".please help
– KUNDAN KUMAR Roy
Nov 12 at 2:28
@KUNDANKUMARRoy I added a change to your preprocessing
– Dani G
Nov 12 at 2:39
add a comment |
I have appliied the same code as given above but it generates error."AttributeError: transform not found".please help
– KUNDAN KUMAR Roy
Nov 12 at 2:28
@KUNDANKUMARRoy I added a change to your preprocessing
– Dani G
Nov 12 at 2:39
I have appliied the same code as given above but it generates error."AttributeError: transform not found".please help
– KUNDAN KUMAR Roy
Nov 12 at 2:28
I have appliied the same code as given above but it generates error."AttributeError: transform not found".please help
– KUNDAN KUMAR Roy
Nov 12 at 2:28
@KUNDANKUMARRoy I added a change to your preprocessing
– Dani G
Nov 12 at 2:39
@KUNDANKUMARRoy I added a change to your preprocessing
– Dani G
Nov 12 at 2:39
add a comment |
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thanks in advance.
– KUNDAN KUMAR Roy
Nov 11 at 15:38
You need to post the code here with inputs and what you have tried so we can reproduce that.
– Franco Piccolo
Nov 11 at 15:40
Welcome to stackoverflow! Please take the tour and read the help pages. Helpful may be "how to ask good questions" and this question checklist. Users here are way more ready to help if you provide minimal, complete, and verifiable example with some input and the desired output.
– Mikhail Stepanov
Nov 11 at 15:46