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Use text classifier on unseen data

0

$begingroup$

I've trained a few models to classify between two categories of text. Logistic regression was the best. Now how can i test it on unseen data?
I tried this:

def train_model():
 classifier.fit(feature_vector_train, label)
 predictions = classifier.predict(feature_vector_valid)
 joblib.dump(classifier, url+name)
...

load_model =joblib.load('my_model.pkl)
result = load_model.score('testx')

It tells me i need a y input. However, if it's new i don't have the label. WHat am i missing?

logistic-regression

share|improve this question

asked 34 mins ago

Bia Calico CatBia Calico Cat

1

New contributor

Bia Calico Cat 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 | 

0

$begingroup$

I've trained a few models to classify between two categories of text. Logistic regression was the best. Now how can i test it on unseen data?
I tried this:

def train_model():
 classifier.fit(feature_vector_train, label)
 predictions = classifier.predict(feature_vector_valid)
 joblib.dump(classifier, url+name)
...

load_model =joblib.load('my_model.pkl)
result = load_model.score('testx')

It tells me i need a y input. However, if it's new i don't have the label. WHat am i missing?

logistic-regression

share|improve this question

asked 34 mins ago

Bia Calico CatBia Calico Cat

1

New contributor

Bia Calico Cat 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've trained a few models to classify between two categories of text. Logistic regression was the best. Now how can i test it on unseen data?
I tried this:

def train_model():
 classifier.fit(feature_vector_train, label)
 predictions = classifier.predict(feature_vector_valid)
 joblib.dump(classifier, url+name)
...

load_model =joblib.load('my_model.pkl)
result = load_model.score('testx')

It tells me i need a y input. However, if it's new i don't have the label. WHat am i missing?

logistic-regression

share|improve this question

asked 34 mins ago

Bia Calico CatBia Calico Cat

1

New contributor

Bia Calico Cat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

def train_model():
 classifier.fit(feature_vector_train, label)
 predictions = classifier.predict(feature_vector_valid)
 joblib.dump(classifier, url+name)
...

load_model =joblib.load('my_model.pkl)
result = load_model.score('testx')

share|improve this question

asked 34 mins ago

Bia Calico CatBia Calico Cat

1

New contributor

Bia Calico Cat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

asked 34 mins ago

Bia Calico CatBia Calico Cat

1

New contributor

Bia Calico Cat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 34 mins ago

Bia Calico CatBia Calico Cat

1

New contributor

Bia Calico Cat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 34 mins ago

Bia Calico CatBia Calico Cat

1

1 Answer
1

active

oldest

votes

0

$begingroup$

Welcome to the forums.

My understanding is that you're wanting to use the previously trained model to label new data points? If so, you'll be wanting to use .predict(X). From sklearn's documentation they say.

All supervised estimators in scikit-learn implement a fit(X, y) method to fit the model > and a predict(X) method that, given unlabeled observations X, returns the predicted
labels y. (Source)

Another note, is that you can't pass direct strings to a model - you'll need to preprocess your data like you did for your training set. Here is a good example of building a classifier and using it to predict new points.

Let me know if you have any questions of I've misunderstood.

share|improve this answer

answered 11 mins ago

James CJames C

362

$endgroup$

add a comment | 

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0

$begingroup$

Welcome to the forums.

My understanding is that you're wanting to use the previously trained model to label new data points? If so, you'll be wanting to use .predict(X). From sklearn's documentation they say.

All supervised estimators in scikit-learn implement a fit(X, y) method to fit the model > and a predict(X) method that, given unlabeled observations X, returns the predicted
labels y. (Source)

Another note, is that you can't pass direct strings to a model - you'll need to preprocess your data like you did for your training set. Here is a good example of building a classifier and using it to predict new points.

Let me know if you have any questions of I've misunderstood.

share|improve this answer

answered 11 mins ago

James CJames C

362

$endgroup$

add a comment | 

0

$begingroup$

Welcome to the forums.

My understanding is that you're wanting to use the previously trained model to label new data points? If so, you'll be wanting to use .predict(X). From sklearn's documentation they say.

All supervised estimators in scikit-learn implement a fit(X, y) method to fit the model > and a predict(X) method that, given unlabeled observations X, returns the predicted
labels y. (Source)

Another note, is that you can't pass direct strings to a model - you'll need to preprocess your data like you did for your training set. Here is a good example of building a classifier and using it to predict new points.

Let me know if you have any questions of I've misunderstood.

share|improve this answer

answered 11 mins ago

James CJames C

362

$endgroup$

add a comment | 

$begingroup$

Welcome to the forums.

My understanding is that you're wanting to use the previously trained model to label new data points? If so, you'll be wanting to use .predict(X). From sklearn's documentation they say.

All supervised estimators in scikit-learn implement a fit(X, y) method to fit the model > and a predict(X) method that, given unlabeled observations X, returns the predicted
labels y. (Source)

Another note, is that you can't pass direct strings to a model - you'll need to preprocess your data like you did for your training set. Here is a good example of building a classifier and using it to predict new points.

Let me know if you have any questions of I've misunderstood.

share|improve this answer

answered 11 mins ago

James CJames C

362

$endgroup$

share|improve this answer

answered 11 mins ago

James CJames C

362

answered 11 mins ago

James CJames C

362

answered 11 mins ago

James CJames C

362