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 vote234 views1 answer
Count entries groupby
Python
1 vote190 views1 answer
Add one to all elements in list python
Python
24 votes167 views6 answers
Windows activate venv
Python
1 vote177 views1 answer
How to count stopwords in df
Python
0 vote182 views1 answer
Python json indented
Python
1 vote199 views3 answers
Pip pyttsx3
Python
1 vote173 views1 answer
Python program to keep your computer awake
Python
1 vote160 views1 answer
Python timestamp shift one day
Python
6 votes170 views3 answers
Python generate list
Python
0 vote271 views1 answer
Getting multiple of 5 python
Python