Root Mean Squared Error (RMSE)?

Question

How to compute Root Mean Squared Error (RMSE)? in regression validation prediction?

Answer 1

Hi @Maricel A. Gueco,

Thank you for reaching out to Microsoft Q&A forum!

To compute the Root Mean Squared Error (RMSE) in regression validation prediction, you can use the mean_squared_error function from the sklearn.metrics library in Python. Here's an example code snippet:

>>> from sklearn.metrics import mean_squared_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_squared_error(y_true, y_pred)
0.375
>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
>>> mean_squared_error(y_true, y_pred)
0.708...
>>> mean_squared_error(y_true, y_pred, multioutput='raw_values')
array([0.41666667, 1.        ])
>>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.825...

The RMSE is a measure of the differences between the predicted(y_pred) and actual values(y_true), with lower values indicating better performance.

See official documentation: sklearn.metrics.mean_squared_error

Also see the training module of Train and evaluate regression models.

Hope this helps. Do let us know if you any further queries.

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