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)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())from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_true, y_pred)
rmse = mean_squared_error(y_true, y_pred, squared = False)