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())