Sklearn rmse

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)

Sklearn rmsle

import numpy as np
from sklearn.metrics import mean_squared_log_error

def rmse(y_true, y_pred):
	np.sqrt(mean_squared_log_error(y_true, y_pred))

Source: kaggle.com

Calculate root mean square error python

def rmse(predictions, targets):
    return np.sqrt(((predictions - targets) ** 2).mean())

Source: stackoverflow.com

Sklearn rmse

from sklearn.metrics import mean_squared_error

rms = mean_squared_error(y_actual, y_predicted, squared=False)

Source: stackoverflow.com

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