skitlearn中的交叉验证和标准化

Question

我想通过K-cross验证找到sklearn分类器的准确性。我可以在没有交叉验证的情况下正常估计准确度。但是，如何改进此代码以进行交叉验证并同时应用StandardScaler？

from sklearn.datasets import load_iris 
from sklearn.cross_validation import train_test_split 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn import metrics 
from sklearn.cross_validation import cross_val_score 
from sklearn.preprocessing import StandardScaler 
from sklearn import svm 
from sklearn.pipeline import Pipeline 
iris = load_iris() 
X = iris.data 
y = iris.target 
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=4) 
pipe_lrSVC = Pipeline([('scaler', StandardScaler()), ('clf', svm.LinearSVC())]) 
pipe_lrSVC.fit(X_train, y_train) 
y_pred = pipe_lrSVC.predict(X_test) 
print(metrics.accuracy_score(y_test, y_pred)) 

Answer 1

只需使用管道作为估计输入cross_val_score：

cross_val_score(pipe_lrSVC, iris.data, iris.target, cv=5)