不确定到底出了什么问题。但是，我的目标是建立一个交叉验证的 python 代码。我知道有各种指标，但我认为我使用的是正确的指标。我没有得到我想要的 CV10 结果，而是收到一个错误：
“标量变量的索引无效”
我在 StackOverflow 上发现了这一点： IndexError: invalid index to scalar variable 当您尝试索引 numpy 标量（例如 numpy.int64 或 numpy.float64）时发生。它与 TypeError 非常相似：'int' object has no attribute '_ getitem _' 当您尝试索引 int 时。
任何帮助，将不胜感激...
我正在尝试关注 :: http://scikit-learn.org/stable/modules/model_evaluation.html
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation
from numpy import genfromtxt
import numpy as np
from sklearn.metrics import accuracy_score
def main():
#read in data, parse into training and target sets
dataset = genfromtxt(open('D:\\CA_DataPrediction_TrainData\\CA_DataPrediction_TrainDataGenetic.csv','r'), delimiter=',', dtype='f8')[1:]
target = np.array( [x[0] for x in dataset] )
train = np.array( [x[1:] for x in dataset] )
#In this case we'll use a random forest, but this could be any classifier
cfr = RandomForestClassifier(n_estimators=10)
#Simple K-Fold cross validation. 10 folds.
cv = cross_validation.KFold(len(train), k=10, indices=False)
#iterate through the training and test cross validation segments and
#run the classifier on each one, aggregating the results into a list
results = []
for traincv, testcv in cv:
pred = cfr.fit(train[traincv], target[traincv]).predict(train[testcv])
results.append(accuracy_score(target[testcv], [x[1] for x in pred]) )
#print out the mean of the cross-validated results
print "Results: " + str( np.array(results).mean() )
if __name__=="__main__":
main()