mean-square-error – Tarik Billa

Is sklearn.metrics.mean_squared_error the larger the better (negated)?

September 10, 2023 by Tarik

The actual function “mean_squared_error” doesn’t have anything about the negative part. But the function implemented when you try ‘neg_mean_squared_error’ will return a negated version of the score. Please check the source code as to how its defined in the source code: neg_mean_squared_error_scorer = make_scorer(mean_squared_error, greater_is_better=False) Observe how the param greater_is_better is set to False. Now … Read more

In which cases is the cross-entropy preferred over the mean squared error? [closed]

April 4, 2023 by Tarik

Cross-entropy is prefered for classification, while mean squared error is one of the best choices for regression. This comes directly from the statement of the problems itself – in classification you work with very particular set of possible output values thus MSE is badly defined (as it does not have this kind of knowledge thus … Read more

Mean Squared Error in Numpy?

January 2, 2023 by Tarik

You can use: mse = ((A – B)**2).mean(axis=ax) Or mse = (np.square(A – B)).mean(axis=ax) with ax=0 the average is performed along the row, for each column, returning an array with ax=1 the average is performed along the column, for each row, returning an array with omitting the ax parameter (or setting it to ax=None) the … Read more