I literally have no idea what you're asking for.
Can anyone assist me on how i go about arranging data that i used as continous input,to data that goes through pair combos for binary output.ie
out
3 5 9 19 11
4 6 7 12 23
3 3 4 6 7 2
to something similar
3 5 9 19 1
4 6 7 12 0 etc
Thanks
I literally have no idea what you're asking for.
"His programming is malfunctioning. It begins! Get your weapons, he's going to become a killbot!!!" - bryangoodrich
I have alot of data that has continuous outputs.
I just wanted to know how i go about permutating ever pair for classifcation.
Literally 200 000 cases.Too much to do manually.
I still don't understand what you mean. What do you mean by permuting every pair for classification?
Are you just saying you have some continuous "predictors" and you want to use those to try to break your data into two different groups - and within each group the data is supposed to be fairly similar to each other?
"His programming is malfunctioning. It begins! Get your weapons, he's going to become a killbot!!!" - bryangoodrich
I do apologise for being very unclear.
Reflecting on my thread it looks lazy and disrespectful of forum protocol.
Ive been studying horse racing and other sport events with inputs ive put together.
I have many samples of input variables for individual horses,irrespective of horse field sizes.
Desired outputs being 1 for 1st, 2 for 2nd etc.Ive normalized the inputs and ran it through PCA.
Im not getting results i'd hoped for.Noisy data also cleansed to an extent.
So i would like to delve into individual race samples and have binary outputs for every paired combo of the horses to interprate the winning horses without having to manually do this.
Horse1 output 1st - 1
horse 2 output 4th - 0
etc
I thought having thousands of horses would have rectified the race field bias.and the lenghts beaten offset.
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