How to create train, test and validation samples from an R data frame?

R ProgrammingServer Side ProgrammingProgramming

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

 Live Demo

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

 Live Demo

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

 Live Demo

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
raja
Published on 10-Oct-2020 13:13:14
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