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

13 votes235 views3 answers
How to change character in string python
Python
1 vote241 views1 answer
How to make a calcukatir in python
Python
1 vote223 views1 answer
Pythonic
Python
2 votes247 views1 answer
Add numpy array to pandas dataframe
Python
0 vote235 views1 answer
Vscode python region folding
Python
0 vote225 views2 answers
How to hyperlink image in blender
Python
10 votes232 views5 answers
Appending to a list python
Python
0 vote244 views1 answer
Replace substrings in python
Python
0 vote251 views1 answer
Django clodinarystorage
Python
1 vote226 views1 answer
Show all numpy array
Python