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 vote310 views1 answer
Python linter
Python
3 votes289 views1 answer
PIL image example
Python
122 votes325 views6 answers
Rename columns in python
Python
0 vote329 views1 answer
Is_isogram
Python
6 votes324 views2 answers
How many nan in array python
Python
0 vote310 views1 answer
Python print functoin
Python
0 vote307 views1 answer
Introduction python graphviz simple graph examples
Python
0 vote315 views1 answer
Not staments python
Python
0 vote305 views1 answer
Python how to make multiple box plots
Python
0 vote359 views1 answer
Two input number sum in python
Python