本文整理汇总了Python中weka.classifiers.Evaluation.kappa方法的典型用法代码示例。如果您正苦于以下问题：Python Evaluation.kappa方法的具体用法？Python Evaluation.kappa怎么用？Python Evaluation.kappa使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weka.classifiers.Evaluation的用法示例。
在下文中一共展示了Evaluation.kappa方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: range
# 需要导入模块: from weka.classifiers import Evaluation [as 别名]
# 或者: from weka.classifiers.Evaluation import kappa [as 别名]
print algo.__class__.__name__
print evaluation.toSummaryString()
confusion_matrix = evaluation.confusionMatrix() # confusion matrix
print "Confusion Matrix:"
for l in confusion_matrix:
print '** ', ','.join('%2d'%int(x) for x in l)
# example to collect an individual statistic for all evaluated classifiers
print "------------------------------------"
print "Example to collect an individual statistic for all evaluated classifiers"
print "Kappa"
for index in range(len(algo_keys)):
evaluation = my_evaluations[index]
key = algo_keys[index]
algo = algo_dict[key]
print algo.__class__.__name__ + ": " + str(evaluation.kappa())
# Example K fold cross validate model against training data
# NOTE: This should be done against test data not training data.
print "Cross validation with 10 folds"
for index in range(len(algo_keys)):
evaluation = my_evaluations[index]
key = algo_keys[index]
algo = algo_dict[key]
output = PlainText() # plain text output for predictions
output.setHeader(data)
buffer = StringBuffer() # buffer to use
output.setBuffer(buffer)
rand = Random(1)
attRange = Range() # no additional attributes output
outputDistribution = Boolean(False) # we don't want distribution