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Text classification: value error couldn't convert str to float

sklearn: vectorizing in cross validation for text classificationsci-kit learn: Reshape your data either using X.reshape(-1, 1)Bug with CalibratedClassifierCV when using a Pipeline with TF-IDF?SVM value error text classificationLabelEncoder: TypeError: '>' not supported between instances of 'float' and 'str'Naive Bayes Classifier using Sklearn.naive_bayes.Bernoulli; how to use model to predict?Feature Mismatch with OneHotEncoder while predicting for a single instance of dataHow can using more n-gram orders decrease accuracy for Multinomial NaiveBayes classifier?Error predicting: X has n features per sample, expecting mWhile applying OneHotEncoder - Error: Could not convert Str to float: C148

.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;

2

Input for random forest classifier trained model for text classification

I am not able to know what should be the input for the trained model after opening the model from the pickle file.

with open('text_classifier', 'rb') as training_model:
 model = pickle.load(training_model)
for message in text:
 message1 = [str(message)]
 pred = model.predict(message1)
 list.append(pred)
return list

Expected output: Non political

Actual output :

ValueError: could not convert string to float: 'RT @ScotNational The
witness admitted that not all damage inflicted on police cars was
caused

scikit-learn word-embedding

share|improve this question

edited Mar 29 at 4:50

Venkatachalam N

6,63766 gold badges1515 silver badges4141 bronze badges

asked Mar 28 at 16:26

Chetan ManjuChetan Manju

1111 bronze badge

add a comment
 | 

2

Input for random forest classifier trained model for text classification

I am not able to know what should be the input for the trained model after opening the model from the pickle file.

with open('text_classifier', 'rb') as training_model:
 model = pickle.load(training_model)
for message in text:
 message1 = [str(message)]
 pred = model.predict(message1)
 list.append(pred)
return list

Expected output: Non political

Actual output :

ValueError: could not convert string to float: 'RT @ScotNational The
witness admitted that not all damage inflicted on police cars was
caused

scikit-learn word-embedding

share|improve this question

edited Mar 29 at 4:50

Venkatachalam N

6,63766 gold badges1515 silver badges4141 bronze badges

asked Mar 28 at 16:26

Chetan ManjuChetan Manju

1111 bronze badge

add a comment
 | 

Input for random forest classifier trained model for text classification

I am not able to know what should be the input for the trained model after opening the model from the pickle file.

with open('text_classifier', 'rb') as training_model:
 model = pickle.load(training_model)
for message in text:
 message1 = [str(message)]
 pred = model.predict(message1)
 list.append(pred)
return list

Expected output: Non political

Actual output :

ValueError: could not convert string to float: 'RT @ScotNational The
witness admitted that not all damage inflicted on police cars was
caused

scikit-learn word-embedding

share|improve this question

edited Mar 29 at 4:50

Venkatachalam N

6,63766 gold badges1515 silver badges4141 bronze badges

asked Mar 28 at 16:26

Chetan ManjuChetan Manju

1111 bronze badge

with open('text_classifier', 'rb') as training_model:
 model = pickle.load(training_model)
for message in text:
 message1 = [str(message)]
 pred = model.predict(message1)
 list.append(pred)
return list

share|improve this question

edited Mar 29 at 4:50

Venkatachalam N

6,63766 gold badges1515 silver badges4141 bronze badges

asked Mar 28 at 16:26

Chetan ManjuChetan Manju

1111 bronze badge

share|improve this question

edited Mar 29 at 4:50

Venkatachalam N

6,63766 gold badges1515 silver badges4141 bronze badges

asked Mar 28 at 16:26

Chetan ManjuChetan Manju

1111 bronze badge

edited Mar 29 at 4:50

Venkatachalam N

6,63766 gold badges1515 silver badges4141 bronze badges

edited Mar 29 at 4:50

Venkatachalam N

6,63766 gold badges1515 silver badges4141 bronze badges

asked Mar 28 at 16:26

Chetan ManjuChetan Manju

1111 bronze badge

asked Mar 28 at 16:26

Chetan ManjuChetan Manju

1111 bronze badge

1 Answer
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1

You need to encode the text as numbers. No machine algorithm can process text directly.

More precisely, you need to use a word embedding (the same used for training the model). Example of common word embeddings are Word2vec, TF-IDF.

I suggest you to play with sklearn.feature_extraction.text.CountVectorizer and sklearn.feature_extraction.text.TfidfTransformer to familiarize yourself with the concept of embedding.

However, if you do not use the same embedding as the one used to train the model you load, there is no way you will obtain good results.

share|improve this answer

answered Mar 28 at 19:24

EskappEskapp

1,9511414 silver badges2727 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thank you sir can i get a link which will help me for the same
  
  – Chetan Manju
  Mar 30 at 14:15
  

  
  
  
  

add a comment
 | 

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1

You need to encode the text as numbers. No machine algorithm can process text directly.

More precisely, you need to use a word embedding (the same used for training the model). Example of common word embeddings are Word2vec, TF-IDF.

I suggest you to play with sklearn.feature_extraction.text.CountVectorizer and sklearn.feature_extraction.text.TfidfTransformer to familiarize yourself with the concept of embedding.

However, if you do not use the same embedding as the one used to train the model you load, there is no way you will obtain good results.

share|improve this answer

answered Mar 28 at 19:24

EskappEskapp

1,9511414 silver badges2727 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thank you sir can i get a link which will help me for the same
  
  – Chetan Manju
  Mar 30 at 14:15
  

  
  
  
  

add a comment
 | 

1

You need to encode the text as numbers. No machine algorithm can process text directly.

More precisely, you need to use a word embedding (the same used for training the model). Example of common word embeddings are Word2vec, TF-IDF.

I suggest you to play with sklearn.feature_extraction.text.CountVectorizer and sklearn.feature_extraction.text.TfidfTransformer to familiarize yourself with the concept of embedding.

However, if you do not use the same embedding as the one used to train the model you load, there is no way you will obtain good results.

share|improve this answer

answered Mar 28 at 19:24

EskappEskapp

1,9511414 silver badges2727 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thank you sir can i get a link which will help me for the same
  
  – Chetan Manju
  Mar 30 at 14:15
  

  
  
  
  

add a comment
 | 

You need to encode the text as numbers. No machine algorithm can process text directly.

More precisely, you need to use a word embedding (the same used for training the model). Example of common word embeddings are Word2vec, TF-IDF.

I suggest you to play with sklearn.feature_extraction.text.CountVectorizer and sklearn.feature_extraction.text.TfidfTransformer to familiarize yourself with the concept of embedding.

However, if you do not use the same embedding as the one used to train the model you load, there is no way you will obtain good results.

share|improve this answer

answered Mar 28 at 19:24

EskappEskapp

1,9511414 silver badges2727 bronze badges

share|improve this answer

answered Mar 28 at 19:24

EskappEskapp

1,9511414 silver badges2727 bronze badges

answered Mar 28 at 19:24

EskappEskapp

1,9511414 silver badges2727 bronze badges

answered Mar 28 at 19:24

EskappEskapp

1,9511414 silver badges2727 bronze badges