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

16 votes208 views6 answers
Python raise exception
Python
7 votes193 views1 answer
Save and load sklearn model PKL
Python
0 vote203 views2 answers
Python3 base64 encode basic authentication
Python
0 vote190 views1 answer
* pattern by python
Python
0 vote210 views1 answer
Python is program running
Python
0 vote214 views1 answer
Python : read all the contents of the file into a string (use of 'with open')
Python
-1 vote211 views6 answers
Mac install python 3.8
Python
0 vote196 views4 answers
Regression model build
Python
-1 vote204 views1 answer
How to make take command in python
Python
6 votes221 views3 answers
How do i get parent directory python
Python