python - 我正在进行分层分割并出现越界错误，我不明白为什么

Question

我正在尝试进行分层随机播放，但我对此很陌生

from sklearn import preprocessing
from sklearn import cross_validation
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.model_selection import StratifiedShuffleSplit

data = featureFormat(my_dataset, features_list, sort_keys = True)
labels, features = targetFeatureSplit(data)
scaler = preprocessing.MinMaxScaler()
features = scaler.fit_transform(features)


split = StratifiedShuffleSplit(n_splits=3, test_size=0.2, random_state=42)
print len(features), len(labels)
for train_index,test_index in split.split(features, labels):
    print("TRAIN:", train_index, "TEST:", test_index)
    features_train,features_test = features_train[train_index],features_test[test_index] 
    labels_train,labels_test = labels_train[train_index],labels_test[test_index]

这是我遇到的错误

尽管限制最高为 143，但它显示为 100

Answer 1

您使用了错误的变量名称。
这些行:

features_train,features_test = features_train[train_index],features_test[test_index] 
labels_train,labels_test = labels_train[train_index],labels_test[test_index]

应该是:

features_train,features_test = features[train_index],features[test_index] 
labels_train,labels_test = labels[train_index],labels[test_index]

您基本上在声明变量之前就使用了它们。您需要对原始特征和标签进行切片。