/Python/From sklearn.metrics import mean_square_error

From sklearn.metrics import 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

From sklearn.metrics import mean_square_error

import mean_squared_error

Similar codes

12 votes227 views2 answers
Python delete directory if exists
Python
0 vote225 views1 answer
Make a tuple
Python
0 vote203 views1 answer
Using shebang python
Python
41 votes199 views6 answers
Python try except
Python
4 votes236 views5 answers
Seaborn countplot
Python
17 votes210 views4 answers
Substring in python
Python
0 vote196 views1 answer
Python - How To Pad String With Spaces
Python
12 votes217 views6 answers
Flask
Python
4 votes197 views3 answers
Docker run python
Python
0 vote192 views1 answer
No limit row pandas
Python