Mean Squared Error in Numpy

Question

Is there a method in numpy for calculating the Mean Squared Error between two matrices   I ve tried searching but found none  Is it under a different name   If there isn t  how do you overcome this  Do you write it yourself or use a different lib

User · Answer

Just for kicks  mse    np linalg norm A-B   2  len A

User · Answer

This isn t part of numpy  but it will work with numpy ndarray objects  A numpy matrix can be converted to a numpy ndarray and a numpy ndarray can be converted to a numpy matrix   from sklearn metrics import mean squared error mse   mean squared error A  B    See Scikit Learn mean squared error for documentation on how to control axis

User · Answer

The standard numpy methods for calculation mean squared error  variance  and its square root  standard deviation  are numpy var   and numpy std    see here and here  They apply to matrices and have the same syntax as numpy mean    I suppose that the question and the preceding answers might have been posted before these functions became available

User · Answer

Another alternative to the accepted answer that avoids any issues with matrix multiplication    def MSE Y  YH        return np square Y - YH  mean     From the documents for np square   Return the element-wise square of the input

User · Answer

Even more numpy  np square np subtract A  B   mean

User · Answer

You can use   mse     A - B   2  mean axis ax    Or  mse    np square A - B   mean axis ax     with ax 0 the average is performed along the row  for each column  returning an array with ax 1 the average is performed along the column  for each row  returning an array with ax None the average is performed element-wise along the array  returning a scalar value

[python] Mean Squared Error in Numpy?

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