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That’s true ;) Using two is enough. And I’m quite surprised by the fact that the choice doesn’t make noticeable difference.
Although by information any combination of two is equivalent, for ML to perform good you not only need to provide enough information, but also to preprocess them properly. e.g. make the variance to 1, average to 0.
the relationship between bft and distance & bullet power is not linear. the higher the power, the bigger 0.1 difference in power affects bft. (plot 1 / (20 - 3x), you’ll see ;) ) therefore if you use power&distance, when power is high, it is weighted too low, and when power is low, it is weighted too high ;) the same thing happens to bft&power though, but anything don’t react to power should be more relevant with bft imo.
Then it may make sense to preprocess power to 1 / (20 - 3 power), whether for distance & power or bft & power. But since power is not distributed evenly, this may not work. Or preprocessing based on the real distribution of bullet power makes sense.
