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Let's suppose I am doing classification and that I have 99 features and another feature that says if the person is male or female.

I have two options viz to build one classifier using all the features or to build two separate classifiers, each built on samples that are either males or females respectively.

Is there any advantage in building two separate models for males and females as compared to building a single model using all the 100 features?

In other words, what scenarios would we have to choose such an option?

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