Is There a Library Function for Root Mean Square Error (Rmse) in Python

scikit-learn: How to calculate root-mean-square error (RMSE) in percentage?

Your implementation of calculate_mape is not working because you are expecting the check_arrays function, which was removed in sklearn 0.16. check_array is not what you want.

This StackOverflow answer gives a working implementation.

Mean Square Error (MSE) Root Mean Square Error (RMSE)

Mean Squared Error:

In statistics, the mean squared error (MSE) or mean squared deviation
(MSD) of an estimator (of a procedure for estimating an unobserved
quantity) measures the average of the squares of the errors.

So for example let's assume you have three datapoints:

Price Predicted
1900  2000
2000  2000
2100  2000

Then the MSE is: 1/3 * ((-100)*(-100)+ (0)*(0) + (100)*(100)) = 1/3 * (20000) = 6000

The perfect one would be 0, but this you will probably not reach. You have to interpret it in comparison with your actual value range.

The RMSE in this case would be: SQRT(6000) = 77,..

This is more intepretable, that means on average you are 77 away from your prediction, which makes sense if you see the three results

How to calculate RMSE without numpy?

Here is how I would do it:

pred = [4, 25, 0.75, 11]
observed = [3, 21, -1.25, 13]
error = [(p - o) for p, o in zip(pred, observed)]
square_error = [e**2 for e in error]
mean_square_error = sum(square_error)/len(square_error)
root_mean_square_error = mean_square_error**0.5

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