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The primary issue is that fowlkes_mallows_score is designed to evaluate clustering and you are trying to apply it to evaluate binary classification.


If you calculated the RMSE on a test set, then it will be a better metric in assessing how well your model will perform in predictions for future observations, i.e. estimating accuracy on an unseen observations. R-squared, as you stated, is the proportion on variance in your training set that's explained by your model fit. Hence, the crucial difference ...


Look at the equations. Both are functions of mean squared error. Any model the outperforms on one will outperform on the other. The danger I see with $R^2$ is that it puts us in a position of thinking of grades in school, yet an $F$-grade $R^2=0.4$ could be quite excellent for some models, while an $A$-grade $R^2=0.95$ could be quite pedestrian for some ...


Your interpretation is correct though I wouldn't say one is 'better' than the other. They both serve different purposes. The first metric I generally check after building my model is MAPE. So I can sense the relative error there with respect to the actual predictions. Though the problem with MAPE is, if there are few outliers in your predictions then your ...

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