All attributes (columns) are equally important by default, they don't get assigned any a priori importance.
However, when you run a modeling algorithm on your data, you might get an assessment of the actual importance according to this machine learning algorithm for every attribute. But that's the result of the analysis. You can't manually change the outcome of most models, and it would make their result worse, not better.
However, if you get a simple model like from a linear regression, you can manually change the coefficients in the formula to emphasize an attribute. It will likely make the model worse in terms of the original success criterion (accuracy, root mean squared error, ...) but it might be better for your use case if you know what you're doing.
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All attributes (columns) are equally important by default, they don't get assigned any a priori importance.
However, when you run a modeling algorithm on your data, you might get an assessment of the actual importance according to this machine learning algorithm for every attribute. But that's the result of the analysis. You can't manually change the outcome of most models, and it would make their result worse, not better.
However, if you get a simple model like from a linear regression, you can manually change the coefficients in the formula to emphasize an attribute. It will likely make the model worse in terms of the original success criterion (accuracy, root mean squared error, ...) but it might be better for your use case if you know what you're doing.
Regards,
Balázs