SGDClassifier accuracy MUCH worse than LinearSVC
$begingroup$
with LinearSVC, I get an accuracy off 0.89:
linearSVC = LinearSVC().fit(X_train, y_train)
y_pred = linearSVC.predict(X_test)
print('accuracy', np.sum(y_pred == y_test) / y_test.shape[0])
Because I need online training, I tried the same with SGDClassifier:
sgd = SGDClassifier().fit(X_train, y_train)
y_pred = sgd.predict(X_test)
print('accuracy', np.sum(y_pred == y_test) / y_test.shape[0])
but now I get 0.004 - what am I missing here? The shape of X_train is (21519, 8255). Of course I do not expect the exact same accuracy, but SGDClassifier basically gets nothing right...
scikit-learn online-learning
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$endgroup$
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$begingroup$
with LinearSVC, I get an accuracy off 0.89:
linearSVC = LinearSVC().fit(X_train, y_train)
y_pred = linearSVC.predict(X_test)
print('accuracy', np.sum(y_pred == y_test) / y_test.shape[0])
Because I need online training, I tried the same with SGDClassifier:
sgd = SGDClassifier().fit(X_train, y_train)
y_pred = sgd.predict(X_test)
print('accuracy', np.sum(y_pred == y_test) / y_test.shape[0])
but now I get 0.004 - what am I missing here? The shape of X_train is (21519, 8255). Of course I do not expect the exact same accuracy, but SGDClassifier basically gets nothing right...
scikit-learn online-learning
New contributor
$endgroup$
add a comment |
$begingroup$
with LinearSVC, I get an accuracy off 0.89:
linearSVC = LinearSVC().fit(X_train, y_train)
y_pred = linearSVC.predict(X_test)
print('accuracy', np.sum(y_pred == y_test) / y_test.shape[0])
Because I need online training, I tried the same with SGDClassifier:
sgd = SGDClassifier().fit(X_train, y_train)
y_pred = sgd.predict(X_test)
print('accuracy', np.sum(y_pred == y_test) / y_test.shape[0])
but now I get 0.004 - what am I missing here? The shape of X_train is (21519, 8255). Of course I do not expect the exact same accuracy, but SGDClassifier basically gets nothing right...
scikit-learn online-learning
New contributor
$endgroup$
with LinearSVC, I get an accuracy off 0.89:
linearSVC = LinearSVC().fit(X_train, y_train)
y_pred = linearSVC.predict(X_test)
print('accuracy', np.sum(y_pred == y_test) / y_test.shape[0])
Because I need online training, I tried the same with SGDClassifier:
sgd = SGDClassifier().fit(X_train, y_train)
y_pred = sgd.predict(X_test)
print('accuracy', np.sum(y_pred == y_test) / y_test.shape[0])
but now I get 0.004 - what am I missing here? The shape of X_train is (21519, 8255). Of course I do not expect the exact same accuracy, but SGDClassifier basically gets nothing right...
scikit-learn online-learning
scikit-learn online-learning
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