Question

开设机器学习课程，我希望将数据分成训练和测试集。我想将其拆分，使用Decisiontree进行训练，然后打印出我的测试集的分数。我的代码中给出了交叉验证参数。有谁看到我做错了什么？

我得到的错误如下：

Traceback (most recent call last):
  File "/home/stephan/ud120-projects/validation/validate_poi.py", line 36, in <module>
    clf = clf.fit(features_train, labels_train)
  File "/home/stephan/.local/lib/python2.7/site-packages/sklearn/tree/tree.py", line 221, in fit
    "number of samples=%d" % (len(y), n_samples))
ValueError: Number of labels=29 does not match number of samples=66

这是我的代码：

import pickle
import sys
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit

data_dict = pickle.load(open("../final_project/final_project_dataset.pkl", "r") )

features_list = ["poi", "salary"]

data = featureFormat(data_dict, features_list)
labels, features = targetFeatureSplit(data)

from sklearn import tree
from sklearn import cross_validation

features_train, labels_train, features_test, labels_test = \
    cross_validation.train_test_split(features, labels, random_state=42, test_size=0.3)



clf = tree.DecisionTreeClassifier()
clf = clf.fit(features_train, labels_train)
print clf.score(features_test, labels_test)

Answer 1

您的变量似乎与train_test_split

的返回模式不匹配

尝试：

features_train, features_test, labels_train, labels_test = ...

Answer 2

您需要在train_ test_split函数中传递test_size = 0.5

train_test_split(...,test_size=0.5,...)

python sklearn cross_validation /标签数量与样本数量不匹配

2 个答案: