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A note on rank correlation and semi-supervised machine learning based measure

机译:关于等级相关和基于半监督机器学习的测度的注释

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This paperwork deals with similarity measure using Kendall's coefficient tau and spearman's coefficient rho. The results for the same ordinal values of two variables that are related and we are also going to describe a note of study in sample data that we got using the semi-supervised machine learning predication. We will use simple linear regression model technique to predict. Here we use Pearson correlation to calculate simple linear regression. Though this two coefficient (rank correlation) looks similar it has some differences which give major change as far as rank correlation results are concerned. And finally we will elaborate which one has more advantage and which is better to use and we will elaborate the note of study in data using simple linear regression model.
机译:本文使用肯德尔系数tau和斯皮尔曼系数rho处理相似性度量。两个相关变量的序数值相同的结果,我们还将使用半监督机器学习谓词描述样本数据中的研究笔记。我们将使用简单的线性回归模型技术进行预测。在这里,我们使用皮尔森相关性来计算简单的线性回归。尽管这两个系数(秩相关)看起来很相似,但是就秩相关结果而言,它们还是有一些差异,这会带来重大变化。最后,我们将阐述哪种优势更大,哪些优势更好,并使用简单的线性回归模型阐述数据研究的注意事项。

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