Sklearn mean square error

How to calculate rmse in linear regression python

actual = [0, 1, 2, 0, 3]
predicted = [0.1, 1.3, 2.1, 0.5, 3.1]

mse = sklearn.metrics.mean_squared_error(actual, predicted)

rmse = math.sqrt(mse)

print(rmse)

Calculate root mean square error python

def rmse(predictions, targets):
    return np.sqrt(((predictions - targets) ** 2).mean())

Source: stackoverflow.com

Sklearn mean square error

from sklearn.metrics import mean_squared_error

Similar codes

0 vote319 views1 answer
Pyplot new figure
Python
1 vote315 views1 answer
Spacy matcher syntax
Python
12 votes305 views6 answers
Python progress bar
Python
0 vote309 views1 answer
Python string with si suffix to number
Python
0 vote288 views1 answer
If list is null python apply any function site:stackoverflow.com
Python
2 votes291 views4 answers
Pygame
Python
0 vote302 views1 answer
Download csv file from jupyter notebook
Python
1 vote321 views1 answer
Selenium.common.exceptions.ElementNotInteractableException: Message: element not interactable
Python
6 votes327 views1 answer
Django desc order
Python Django
-1 vote282 views2 answers
String template python
Python