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How to create train, test and validation samples from an R data frame?
To create predictive models, it is necessary to create three subsets of a data set for the purpose of training the model, testing the model and checking the validation of the model. These subsets are usually called train, test and validation. For this purpose, we can use different type of sampling methods and the most common is random sampling. In the below example, you can see how it can be done.
Consider the mtcars data set in base R −
Example
data(mtcars) str(mtcars)
Output
'data.frame':32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... $ disp: num 160 160 108 258 360 ... $ hp : num 110 110 93 110 175 105 245 62 95 123 ... $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... $ wt : num 2.62 2.88 2.32 3.21 3.44 ... $ qsec: num 16.5 17 18.6 19.4 17 ... $ vs : num 0 0 1 1 0 1 0 1 1 1 ... $ am : num 1 1 1 0 0 0 0 0 0 0 ... $ gear: num 4 4 4 3 3 3 3 4 4 4 ... $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
Example
head(mtcars)
Output
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
Creating train, test and validation samples −
Example
Samples<-sample(seq(1,3),size=nrow(mtcars),replace=TRUE,prob=c(0.8,0.2,0.2)) Train<-mtcars[Samples==1,] Test<-mtcars[Samples==2,] Validate<-mtcars[Samples==3,] Train
Output
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Example
Test
Output
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Example
Validate
Output
mpg cyl disp hp drat wt qsec vs am gear carb Merc 230 22.8 4140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Updated on: 10-Oct-2020
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