问显示分类错误的实例

EN

>>> from sklearn import cross_validation, datasets, svm
>>> X, y = datasets.make_classification()
>>> X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y)
>>> clf = svm.LinearSVC().fit(X_train, y_train)
>>> y_pred = clf.predict(X_test)

>>> misclassified_samples = X_test[y_test != y_pred]

>>> import numpy as np
>>> np.flatnonzero(y_test != y_pred)
array([ 0, 20, 22])

from sklearn.svm import LinearSVC
X_train=your_train_data
y_train=your_train_lables
X_test=your_test_data #should be around 30% of you your data
y_test=your_test_labels
svm = LinearSVC()
svm.fit(X_train, y_train)
for item, label in zip(X_test, y_test):
    result = svm.predict([item])
    if result != label:
        print "predicted label %s, but true label is %s" % (result, label)