from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
data, target, test_size=0.25, random_state=0)
from sklearn.model_selection import cross_val_score, KFold
from scipy.stats import sem
def evaluate_cross_validation(clf, X, y, K):
# create a k-fold cross validation iterator
cv = KFold( K , shuffle=True, random_state=0)
# by default the score used is the one returned by score method of the estimator (accuracy)
scores = cross_val_score(clf, X, y, cv=cv)
print (scores)
print ("Mean score: {0:.3f} (+/-{1:.3f})".format(
np.mean(scores), sem(scores)))
evaluate_cross_validation(svc_1, X_train, y_train, 5)
from sklearn import metrics
def train_and_evaluate(clf, X_train, X_test, y_train, y_test):
clf.fit(X_train, y_train)
print ("Accuracy on training set:")
print (clf.score(X_train, y_train))
print ("Accuracy on testing set:")
print (clf.score(X_test, y_test))
y_pred = clf.predict(X_test)
print ("Classification Report:")
print (metrics.classification_report(y_test, y_pred))
print ("Confusion Matrix:")
print (metrics.confusion_matrix(y_test, y_pred))
train_and_evaluate(svc_1, X_train, X_test, y_train, y_test)
random_image_button = Button(description="New image!")
def display_face_and_prediction(b):
index = randint(0, 400)
face = faces.images[index]
display_face(face)
print("this person is smiling: {0}".format(svc_1.predict(faces.data[index, :])==1))
random_image_button.on_click(display_face_and_prediction)
display(random_image_button)
display_face_and_prediction(0)
, когда я запускаю код, начинающийся с random_image_button = Button(description="New image!"), он выдает мне следующую ошибку:
ValueError: Ожидаемый 2D-массив, вместо него получен 1D-массив: array = [0.31818181 0.40082645 0.49173555... 0,14049587 0,14876033 0,15289256].Измените ваши данные, используя array.reshape (-1, 1), если ваши данные имеют один элемент, или array.reshape (1, -1), если он содержит один образец.
Как я могу это исправить?