from sklearn metrics import mean square error

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1actual = [0, 1, 2, 0, 3]
2predicted = [0.1, 1.3, 2.1, 0.5, 3.1]
3
4mse = sklearn.metrics.mean_squared_error(actual, predicted)
5
6rmse = math.sqrt(mse)
7
8print(rmse)

1def rmse(predictions, targets):
2    return np.sqrt(((predictions - targets) ** 2).mean())

1import mean_squared_error

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