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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.


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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 ...


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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 ...


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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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