/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

2 votes287 views1 answer
Not x axis labels python
Python
1 vote299 views1 answer
How to run python file from cmd in dockerfile
Python
6 votes279 views2 answers
Flask get ip address of request
Python
1 vote266 views1 answer
Hide particular attribute in django admin
Python
0 vote275 views1 answer
Apply strip() a column in pandas
Python
8 votes263 views3 answers
Pip install safely in jupyter
Python
28 votes214 views2 answers
Np zeroes
Python
51 votes265 views6 answers
Python remove repeated elements from list
Python
0 vote277 views1 answer
Context manager requests python
Python
54 votes261 views3 answers
Python add up values in list
Python