I am trying to build a decison tree model in R having both categorical and numerical variables.Some categorical variables have 3 levels , so can I just use as.factor and then use in my model? I tried to use model.matrix but my doubt is model.matrix converts the variable in numeric values of 0s and 1s and splitting happens on basis of these numeric values. For eg if Color has 3 level- blue,red,green, the splitting rule will look like color_green < 0.5 instead it should always take 0s and 1s only.
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Please visit [How to make a great R reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example). Providing some sample data with whatever code you have tried will help you to get the solution faster. – UseR10085 Jul 23 '20 at 12:21
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If you are asking whether you can use factors to build an rpart decision tree. Then yes. See below example from the documentation. Note that there are a lot of possible packages for decision trees.
library(rpart)
rpart(Reliability ~ ., data=car90)
#> n=76 (35 observations deleted due to missingness)
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 76 53 average (0.2 0.12 0.3 0.11 0.28)
#> 2) Country=Germany,Korea,Mexico,Sweden,USA 49 29 average (0.31 0.18 0.41 0.1 0)
#> 4) Tires=145,155/80,165/80,185/80,195/60,195/65,195/70,205/60,215/65,225/75,275/40 17 9 Much worse (0.47 0.29 0 0.24 0) *
#> 5) Tires=175/70,185/65,185/70,185/75,195/75,205/70,205/75,215/70 32 12 average (0.22 0.12 0.62 0.031 0)
#> 10) HP.revs< 4650 13 7 Much worse (0.46 0.23 0.31 0 0) *
#> 11) HP.revs>=4650 19 3 average (0.053 0.053 0.84 0.053 0) *
#> 3) Country=Japan,Japan/USA 27 6 Much better (0 0 0.11 0.11 0.78) *
str(car90)
#> 'data.frame': 111 obs. of 34 variables:
#> $ Country : Factor w/ 10 levels "Brazil","England",..: 5 5 4 4 4 4 10 10 10 NA ...
#> $ Disp : num 112 163 141 121 152 209 151 231 231 189 ...
#> $ Disp2 : num 1.8 2.7 2.3 2 2.5 3.5 2.5 3.8 3.8 3.1 ...
#> $ Eng.Rev : num 2935 2505 2775 2835 2625 ...
#> $ Front.Hd : num 3.5 2 2.5 4 2 3 4 6 5 5.5 ...
#> $ Frt.Leg.Room: num 41.5 41.5 41.5 42 42 42 42 42 41 41 ...
#> $ Frt.Shld : num 53 55.5 56.5 52.5 52 54.5 56.5 58.5 59 58 ...
#> $ Gear.Ratio : num 3.26 2.95 3.27 3.25 3.02 2.8 NA NA NA NA ...
#> $ Gear2 : num 3.21 3.02 3.25 3.25 2.99 2.85 2.84 1.99 1.99 2.33 ...
#> $ HP : num 130 160 130 108 168 208 110 165 165 101 ...
#> $ HP.revs : num 6000 5900 5500 5300 5800 5700 5200 4800 4800 4400 ...
#> $ Height : num 47.5 50 51.5 50.5 49.5 51 49.5 50.5 51 50.5 ...
#> $ Length : num 177 191 193 176 175 186 189 197 197 192 ...
#> $ Luggage : num 16 14 17 10 12 12 16 16 16 15 ...
#> $ Mileage : num NA 20 NA 27 NA NA 21 NA 23 NA ...
#> $ Model2 : Factor w/ 21 levels ""," Turbo 4 (3)",..: 1 1 1 1 1 1 1 14 13 1 ...
#> $ Price : num 11950 24760 26900 18900 24650 ...
#> $ Rear.Hd : num 1.5 2 3 1 1 2.5 2.5 4.5 3.5 3.5 ...
#> $ Rear.Seating: num 26.5 28.5 31 28 25.5 27 28 30.5 28.5 27.5 ...
#> $ RearShld : num 52 55.5 55 52 51.5 55.5 56 58.5 58.5 56.5 ...
#> $ Reliability : Ord.factor w/ 5 levels "Much worse"<"worse"<..: 5 5 NA NA 4 NA 3 3 3 NA ...
#> $ Rim : Factor w/ 6 levels "R12","R13","R14",..: 3 4 4 3 3 4 3 3 3 3 ...
#> $ Sratio.m : num NA NA NA NA NA NA NA NA NA NA ...
#> $ Sratio.p : num 0.86 0.96 0.97 0.71 0.88 0.78 0.76 0.83 0.87 0.88 ...
#> $ Steering : Factor w/ 3 levels "manual","power",..: 2 2 2 2 2 2 2 2 2 2 ...
#> $ Tank : num 13.2 18 21.1 15.9 16.4 21.1 15.7 18 18 16.5 ...
#> $ Tires : Factor w/ 30 levels "145","145/80",..: 16 20 20 8 17 28 13 23 23 22 ...
#> $ Trans1 : Factor w/ 4 levels "","man.4","man.5",..: 3 3 3 3 3 3 1 1 1 1 ...
#> $ Trans2 : Factor w/ 4 levels "","auto.3","auto.4",..: 3 3 2 2 3 3 2 3 3 3 ...
#> $ Turning : num 37 42 39 35 35 39 41 43 42 41 ...
#> $ Type : Factor w/ 6 levels "Compact","Large",..: 4 3 3 1 1 3 3 2 2 NA ...
#> $ Weight : num 2700 3265 2935 2670 2895 ...
#> $ Wheel.base : num 102 109 106 100 101 109 105 111 111 108 ...
#> $ Width : num 67 69 71 67 65 69 69 72 72 71 ...
Chuck P
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