• 可以在与用于训练的数据不同的数据上训练和测试模型。
• 响应值对于测试数据集是已知的，因此可以评估预测
• 测试精度是比样本外性能的训练准确性更好的估计。

# load the iris dataset as an example

from sklearn.datasets import load_iris

iris = load_iris()

# store the feature matrix (X) and response vector (y)

X = iris.data

y = iris.target

# splitting X and y into training and testing sets

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1)

# printing the shapes of the new X objects

print(X_train.shape)

print(X_test.shape)

# printing the shapes of the new y objects

print(y_train.shape)

print(y_test.shape)

输出：

(90L, 4L)
(60L, 4L)
(90L,)
(60L,)