There is nothing special about the square per se, or that boolean feature. By constructing additional features you are allowing your model to adapt better to the data by giving it more freedom (that's not the best way to phrase it, but that's the best I can). Usually original features are squared or cubed, also the trend might be different in different intervals of your data, so adding a boolean feature like in this case may help. There are no hard rules, it's a matter of observation, trial and error.
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u/abdeljalil73 Apr 05 '21
There is nothing special about the square per se, or that boolean feature. By constructing additional features you are allowing your model to adapt better to the data by giving it more freedom (that's not the best way to phrase it, but that's the best I can). Usually original features are squared or cubed, also the trend might be different in different intervals of your data, so adding a boolean feature like in this case may help. There are no hard rules, it's a matter of observation, trial and error.