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RMSE, MSE, MAE, SD brief introduction

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Posted on 2025-2-20 14:09:48 | | | |
RMSE (Root Mean Square Error)

Measures the deviation between observations and true values.

It is often used as a measure of the predictive results of machine learning models.



MSE (Mean Square Error) mean square error

MSE is the square of the difference between the true value and the predicted value, and then summed and averaged.

It is convenient to derive by the form of square, so it is often used as a loss function of linear regression. The lower the MSE value, the better the accuracy of the prediction model in describing the experimental data.



MAE (Mean Absolute Error) mean absolute error

is the average of the absolute error.

It can better reflect the actual situation of the error of the predicted value.



SD (Standard Deviation) standard deviation

Arithmetic mean root of variance.

Used to measure the degree of discretization of a set of values.





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 Landlord| Posted on 2025-2-20 14:37:07 |
MSE (Mean Square Error) is calculated by mean square error

Python version

.NET/C# version (both implementations)



 Landlord| Posted on 2025-2-20 14:43:08 |
Math.NET statistical function class

Math.NET the basic statistical classes and their functions in the MathNet.Numerics.Statistics namespace are introduced as follows, and the methods in static classes can basically be used directly as extended methods:

1. Statistics class, basic data set statistics, such as minimum, maximum, mean, population variance, standard deviation, etc. For static classes, note that Statistics is an overall statistical class, and many of its functions are called separately according to the type of dataset.

2. StreamingStatistics, static class, is the statistics of streaming datasets, suitable for some large data sets, cannot be read into memory at one time;

3. ArrayStatistics, a static class, is a statistical of ordinary unsorted array datasets, which are loaded in memory at one time, so it is more convenient to calculate.

4. SortedArrayStatistics, a static class, is the statistics of a sorted array dataset;

5. DescriptiveStatistics, a non-static class, has a similar function to the Statistics class, but the difference is that Statistics is a static method, calculated one by one, and when the class is initialized, all indicators can be calculated at one time and obtained directly through attributes.

6. RunningStatistics, non-static class, has similar functions to the Statistics class, but allows data to be dynamically updated and calculated again;

Reference:

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 Landlord| Posted on 2025-2-20 14:48:23 |
RMSE (Root Mean Square Error)

python version

.NET/C# version

As shown below:



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