sklearn rmse

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))

from sklearn.metrics import mean_squared_error

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

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