I have a training dataset as you can see below:
'data.frame': 229907 obs. of 19 variables:
$ categories : Factor w/ 2061 levels "","Accessories,Fashion,Shopping,Cosmetics & Beauty Supply,Beauty & Spas",..: 253 1541 1720 1647 38 396 522 1727 482 641 ...
$ city : Factor w/ 61 levels "Ahwatukee","Anthem",..: 18 38 38 38 38 38 38 51 31 43 ...
$ latitude : num 33.3 33.5 33.5 33.5 33.5 ...
$ longitude : num -112 -112 -112 -112 -112 ...
$ open : Factor w/ 2 levels "False","True": 2 2 2 2 2 2 2 2 2 2 ...
$ review_count.x : int 26 127 130 26 8 229 453 24 3 126 ...
$ stars.x : num 4.5 3.5 4 4 4.5 3.5 4 4 2.5 3.5 ...
$ state : Factor w/ 4 levels "AZ","CA","CO",..: 1 1 1 1 1 1 1 1 1 1 ...
$ date : Factor w/ 2504 levels "2005-03-07","2005-03-08",..: 2031 1649 1936 1936 2001 1936 1936 2312 2056 1874 ...
$ stars.y : int 5 4 4 5 4 3 5 5 1 4 ...
$ votes_cool : int 0 0 1 0 0 0 1 1 0 0 ...
$ votes_funny : int 0 0 1 0 0 0 1 1 1 0 ...
$ votes_useful : int 0 0 1 0 1 0 2 1 2 0 ...
$ average_stars : num 5 4.67 4.43 4.43 4.43 4.43 4.43 4.43 2.75 3.65 ...
$ name.y : Factor w/ 8323 levels "a","a.","A","A.",..: 3841 6354 7263 7263 7263 7263 7263 7263 5372 6556 ...
$ review_count.y : int 2 4 7 7 7 7 7 7 4 20 ...
$ Total_votes_cool_user : int 1 0 4 4 4 4 4 4 0 7 ...
$ Total_votes_funny_user : int 0 0 3 3 3 3 3 3 1 5 ...
$ Total_votes_useful_user: int 2 0 6 6 6 6 6 6 3 32 ...
My goal is to apply the randomForest algorithm, but randomForest only embrace factors till level 53. Any suggestion to solve this? I have thought about turning all categorical values into integers, but I think it would not help for thereafter prediction efficiency desired. Also I have had problems with NA values. I used rfImput for replacing them and I also had the same problems.
Thanks,
Many of your "factors" are not really factors at all. date
is not a factor and should be converted to date. name
should be a string. The only one that probably could be seen as a factor is category
but not as it is now. You need to parse it and separate all the categories. A link to help you out (maybe): https://www.stat.berkeley.edu/classes/s133/factors.html . userid
is not a factor (since possibly each observation has a different value), and so on.
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