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Bookish Mass
Bookish Mass

Reputation: 61

SVM problem - name 'model_SVC' is not defined

I have a problem with this code:

    from sklearn import svm
    model_SVC = SVC()
    model_SVC.fit(X_scaled_df_train, y_train)
    svm_prediction = model_SVC.predict(X_scaled_df_test)

The error message is

NameError
Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_14392/1339209891.py in ----> 1 svm_prediction = model_SVC.predict(X_scaled_df_test)

NameError: name 'model_SVC' is not defined

Any ideas?

Upvotes: 3

Views: 5586

Answers (3)

Meet Thakor
Meet Thakor

Reputation: 1

I came across this post while coding with the help of tutorialspoint.com where i was learning about svm and its usage in python implementing in A.I. the code was

Svc_classifier = svm_classifier.SVC(kernel='linear', 
C=C, decision_function_shape = 'ovr').fit(X, y)
Z = svc_classifier.predict(X_plot)
Z = Z.reshape(xx.shape)

so revised code would be

svc_classifier = svm.SVC(kernel='linear', 
C=C, decision_function_shape = 'ovr').fit(X, y)
Z = svc_classifier.predict(X_plot)
Z = Z.reshape(xx.shape)

Upvotes: 0

blackraven
blackraven

Reputation: 5597

The line from sklearn import svm was incorrect. The correct way is

from sklearn.svm import SVC

The documentation is sklearn.svm.SVC. And when I choose this model, I'm mindful of the dataset size. Extracted:

The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of samples. For large datasets consider using LinearSVC instead.

from sklearn.svm import LinearSVC

For more info you could read When should one use LinearSVC or SVC?

Upvotes: 1

s510
s510

Reputation: 2822

use:

from sklearn.svm import SVC

Upvotes: 2

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