均方根误差（RMSE）| 机器学习

Hello learners, welcome to yet another article on machine learning. Today we would be looking at one of the methods to determine the accuracy of our model in predicting the target values. All of you reading this article must have heard about the term RMS i.e. Root Mean Square and you might have also used RMS values in statistics as well. In machine Learning when we want to look at the accuracy of our model we take the root mean square of the error that has occurred between the test values and the predicted values mathematically:

大家好，欢迎阅读关于机器学习的另一篇文章。 今天，我们将研究确定模型预测目标值准确性的方法之一。 阅读本文的所有人都必须听说过RMS(即均方根)一词，并且您可能在统计数据中也使用过RMS值。 在机器学习中，当我们要查看模型的准确性时，我们用数学上的平均值来计算测试值和预测值之间出现的误差的均方根：

For a single value:

对于单个值：

    Let a= (predicted value- actual value) ^2
    Let b= mean of a = a (for single value)
    Then RMSE= square root of b 

For a wide set of values RMSE is defined as follows:

对于广泛的值，RMSE定义如下：

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