如何在scikitlearn中正确执行交叉验证？

Question

我正在尝试对k-nn分类器进行交叉验证，我不知道下面两种方法中哪一种正确地进行交叉验证。在

training_scores = defaultdict(list)
validation_f1_scores = defaultdict(list)
validation_precision_scores = defaultdict(list)
validation_recall_scores = defaultdict(list)
validation_scores = defaultdict(list)

def model_1(seed, X, Y):
    np.random.seed(seed)
    scoring = ['accuracy', 'f1_macro', 'precision_macro', 'recall_macro']
    model = KNeighborsClassifier(n_neighbors=13)

    kfold = StratifiedKFold(n_splits=2, shuffle=True, random_state=seed)
    scores = model_selection.cross_validate(model, X, Y, cv=kfold, scoring=scoring, return_train_score=True)
    print(scores['train_accuracy'])
    training_scores['KNeighbour'].append(scores['train_accuracy'])
    print(scores['test_f1_macro'])
    validation_f1_scores['KNeighbour'].append(scores['test_f1_macro'])
    print(scores['test_precision_macro'])
    validation_precision_scores['KNeighbour'].append(scores['test_precision_macro'])
    print(scores['test_recall_macro'])
    validation_recall_scores['KNeighbour'].append(scores['test_recall_macro'])
    print(scores['test_accuracy'])
    validation_scores['KNeighbour'].append(scores['test_accuracy'])

    print(np.mean(training_scores['KNeighbour']))
    print(np.std(training_scores['KNeighbour']))
    #rest of print statments

第二个模型中的for循环似乎是多余的。在

^{pr2}$

我使用的是StratifiedKFold，我不确定是否需要像在模型2函数中那样使用循环，还是在我们传递{}作为参数时，cross_validate函数是否已经使用了拆分。在

我没有调用fit方法，可以吗？cross_validate是自动调用还是在调用cross_validate之前需要先调用fit？在

最后，如何创建混淆矩阵？我是否需要为每个折叠创建它？如果是，如何计算最终/平均混淆矩阵？在

Answer 1

model_1是正确的。在

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html

cross_validate(estimator, X, y=None, groups=None, scoring=None, cv=’warn’, n_jobs=None, verbose=0, fit_params=None, pre_dispatch=‘2*n_jobs’, return_train_score=’warn’, return_estimator=False, error_score=’raise-deprecating’)

在哪里

estimator是实现“fit”的对象。它将被调用以使模型适合火车折叠。在

cv：是一个交叉验证生成器，用于生成训练和测试拆分。在

如果你按照sklearn文档中的例子

cv_results = cross_validate(lasso, X, y, cv=3, return_train_score=False) cv_results['test_score'] array([0.33150734, 0.08022311, 0.03531764])

您可以看到，模型lasso在列车拆分的每个折叠中被拟合了3次，在测试拆分中也被验证了3次。您可以看到报告了验证数据的测试分数。在

Keras模型的交叉验证

Keras提供了使Keras模型与sklearn交叉验证方法兼容的包装器。您必须使用KerasClassifier包装keras模型

from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import KFold, cross_validate
from keras.models import Sequential
from keras.layers import Dense
import numpy as np

def get_model():
    model = Sequential()
    model.add(Dense(2, input_dim=2, activation='relu')) 
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

model = KerasClassifier(build_fn=get_model, epochs=10, batch_size=8, verbose=0)
kf = KFold(n_splits=3, shuffle=True)

X = np.random.rand(10,2)
y = np.random.rand(10,1)

cv_results = cross_validate(model, X, y, cv=kf, return_train_score=False)

print (cv_results)

Answer 2

documentation可以说是您在此类问题中最好的朋友；从这个简单的示例中可以明显看出，您既不应该使用for循环，也不应该使用对fit的调用。调整示例以使用KFold方法：

from sklearn.model_selection import KFold, cross_validate
from sklearn.datasets import load_boston
from sklearn.tree import DecisionTreeRegressor

X, y = load_boston(return_X_y=True)
n_splits = 5
kf = KFold(n_splits=n_splits, shuffle=True)

model = DecisionTreeRegressor()
scoring=('r2', 'neg_mean_squared_error')

cv_results = cross_validate(model, X, y, cv=kf, scoring=scoring, return_train_score=False)
cv_results

结果：

^{pr2}$

how can I create confusion matrix? Do I need to create it for each fold

没有人能告诉你你是否需要为每一个折叠创建一个混淆矩阵-这是你的选择。如果您选择这样做，最好跳过cross_validate并“手动”执行程序-请参阅How to display confusion matrix and report (recall, precision, fmeasure) for each cross validation fold中的答案。在

if yes, how can the final/average confusion matrix be calculated?

不存在“最终/平均”混淆矩阵；如果您想计算除链接答案中描述的k个（每个k折一个）之外的任何内容，则需要有一个单独的验证集。。。在