On Thu, Jun 6, 2019 at 12:21 AM Philippe Michel <[email protected]> wrote:
> Interesting diagram. I suppose the hc_0 and hc_1 prefixes mean the > inputs are for the player on roll and for the other respectively ? > Yes! hc is for "hand crafted" features, the cleaned dataset I used also have features prefixed by "b" for "base inputs". 0 is for the player on roll, and 1 is for the opponent. > The sum of the values seems to be about 0.5. Should the sum be 1 and the > basic inputs amount for the complement ? If this is the case, do you > have the actual sum for the listed inputs ? > No, the diagram is not normalized. The abscissa is the drop of error-rate (I think it is a mean_absolute_error) when randomizing the samples of that feature. I can see if I can make a corresponding plot with the base inputs. > PIPLOSS for one of the players is indeed at the top of the list, but > aggregated for both players MOBILITY seems to be about equal. The second > one, ENTER is interesting, it is 0 when the player doesn't have a man on > the bar so, when it matters, it may well be the most important input. > Sure. I'll keep that in mind. > The first random ideas that come to mind : > > - could it worthwile to add a few of the complex features like MOBILITY > and > ENTER to the pruning nets ? > > - the paper by Berliner mentionned by Ian in another follow-up describes > his efforts to improve PIPLOSS from a simple but not that good algorithm > to more or less what we have. Since, according to your analysis, the > value of this input is very asymetrical between the players, maybe a > simpler version could be used for at least one of them. That's assuming > one can come up with something approximate that is much faster but not > too less accurate. > Sure, I also guess we can look at all the different features, and maybe even come up with some new ideas. Having a good dataset as the one you have gathered (Thanks Philippe!) makes it really simple to train new neural networks and we can try out a lot of features. I can now train a contact neural network within a few minutes. Best regards, -Øystein
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