Question

我正在通过Python Data Science Essentials (2nd Edition)。

本书提供以下代码：

chosen_random_state = 1
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.30, ran-dom_state=chosen_random_state)
print ("(X train shape %s, X test shape %s, \ny train shape %s, y test shape %s" \
% (X_train.shape, X_test.shape, y_train.shape, y_test.shape))
h1.fit(X_train,y_train)
print (h1.score(X_test,y_test))

当我尝试运行它时，我收到以下错误：

----------------------------------------------- ---------------------------- NameError Traceback（最近一次调用   最后）in（）         1 selected_random_state = 1   ----＆GT; 2 X_train，X_test，y_train，y_test = cross_validation.train_test_split（X，y，test_size = 0.30，   random_state = chosen_random_state）         3打印（“（X列车形状％s，X测试形状％s，\ n列车形状％s，y测试形状％s”％（X_train.shape，X_test.shape，y_train.shape，   y_test.shape））         4 h1.fit（X_train，y_train）         5打印（h1.score（X_test，y_test））

NameError：未定义名称'cross_validation'

我怀疑我可能要导入一本书未提及的图书馆。我搜索了手册但找不到这个功能。这是我需要创建的功能，还是有人可以指向相关的库？

Answer 1

MouseManager& GameManager::getMouseManager(){
    return mouseManager;
}

的{p} cross_validation子模块为deprecated。您应该使用sklearn instead

module_selection

Answer 2

您必须导入：

from sklearn.svm.libsvm import cross_validation

Answer 3

您应该导入

from sklearn import cross_validation

确保你已经安装了sklearn。有关如何安装

的说明，请参阅this

Answer 4

这应该可以解决您的问题。试试

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, ran-dom_state=chosen_random_state)

print ("(X train shape %s, X test shape %s, \ny train shape %s, y test shape %s" \
        % (X_train.shape, X_test.shape, y_train.shape, y_test.shape))

h1.fit(X_train,y_train)

Answer 5

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.30, ran-dom_state=chosen_random_state)
print ("(X train shape %s, X test shape %s, \ny train shape %s, y test shape %s" \
% (X_train.shape, X_test.shape, y_train.shape, y_test.shape))
h1.fit(X_train,y_train)   
 X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.30, ran-dom_state=chosen_random_state)
    print ("(X train shape %s, X test shape %s, \ny train shape %s, y test shape %s" \
    % (X_train.shape, X_test.shape, y_train.shape, y_test.shape))
    h1.fit(X_train,y_train)

Scikit learn（Python 3.5）：我是否需要导入库才能使其工作？

5 个答案: