8 examples of 'accuracy sklearn' in Python

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52def test_accuracy(clf, X_test, y_test):
53 X_test = np.array([item for sublist in X_test for item in sublist])
54 y_test = np.array([item for sublist in y_test for item in sublist])
55 return clf.score(X_test, y_test)
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7def accuracy(params):
8 clf = RandomForestClassifier(**params)
9 clf.fit(x_train,y_train)
10 return clf.score(x_test, y_test)
114def accuracy(y_test, y_pred):
115 """Computes the accuracy score.
116
117 Args:
118 y_test: np.array 1-D array of true class labels
119 y_pred: np.array 1-D array of predicted class labels
120
121 Returns:
122 accuracy: float
123 accuracy score
124 """
125 return metrics.accuracy_score(y_test, y_pred)
26def accuracy_score(self, x_test, y_test):
27 estimation = self.model.predict(x_test)
28 return acc_func(estimation,y_test)
425@torch.no_grad()
426def compute_accuracy_classifier(clf, data_train, labels_train, data_test, labels_test):
427 clf.fit(data_train, labels_train)
428 # Predicting the labels
429 y_pred_test = clf.predict(data_test)
430 y_pred_train = clf.predict(data_train)
431
432 return (
433 (
434 compute_accuracy_tuple(labels_train, y_pred_train),
435 compute_accuracy_tuple(labels_test, y_pred_test),
436 ),
437 y_pred_test,
438 )
29def testAccuracy(data_test, label_test, kNN):
30 return kNN.score(data_test, label_test)
52def get_accuracy(self, x_test, y_test, keep_prob=1.0):
53 return self.sess.run(self.accuracy, feed_dict={self.X: x_test, self.Y: y_test, self.keep_prob: keep_prob})
29@unhot
30def accuracy(actual, predicted):
31 return 1.0 - classification_error(actual, predicted)

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