Is there a library function for Root mean square error (RMSE) in python?
python, scikit-learn, scipy
Solution
sklearn >= 0.22.0
`sklearn.metrics` has a `mean_squared_error` function with a `squared` kwarg (defaults to `True`). Setting `squared` to `False` will return the RMSE.
from sklearn.metrics import mean_squared_error
rms = mean_squared_error(y_actual, y_predicted, squared=False)
sklearn < 0.22.0
`sklearn.metrics` has a `mean_squared_error` function. The RMSE is just the square root of whatever it returns.
from sklearn.metrics import mean_squared_error
from math import sqrt
rms = sqrt(mean_squared_error(y_actual, y_predicted))
Problem
I know I could implement a root mean squared error function like this: ``` def rmse(predictions, targets): return np.sqrt(((predictions - targets) ** 2).mean()) ``` What I'm looking for if this rmse function is implemented in a library somewhere, perhaps in scipy or scikit-learn?