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

1 vote327 views1 answer
How to distribute a dataset in train and test using scikit
Python
0 vote364 views1 answer
Split string by special characters python
Python
11 votes348 views1 answer
Scikit learn svm
Python
1 vote356 views1 answer
Python increase one item in list
Python
1 vote308 views1 answer
Python close all plot figures
Python
0 vote380 views1 answer
Flask session auto logout in 5 mins
Python
1 vote353 views1 answer
How to open dialog box to select files in python
Python
38 votes346 views2 answers
How to file in python
Python
0 vote331 views6 answers
List comprehension
Python
0 vote363 views1 answer
How to make a window in pyga,e
Python