Stratified sampling dataset¶

In [1]:

# DAL ToolBox
# version 1.0.777

source("https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R")

#loading DAL
load_library("daltoolbox")

Loading required package: daltoolbox

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 


Attaching package: ‘daltoolbox’


The following object is masked from ‘package:base’:

    transform

In [2]:

iris <- datasets::iris
head(iris)
table(iris$Species)

A data.frame: 6 × 5
	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	<dbl>	<dbl>	<dbl>	<dbl>	<fct>
1	5.1	3.5	1.4	0.2	setosa
2	4.9	3.0	1.4	0.2	setosa
3	4.7	3.2	1.3	0.2	setosa
4	4.6	3.1	1.5	0.2	setosa
5	5.0	3.6	1.4	0.2	setosa
6	5.4	3.9	1.7	0.4	setosa

    setosa versicolor  virginica 
        50         50         50

Dividing a dataset with training and test¶

In [3]:

#using random sampling
tt <- train_test(sample_stratified("Species"), iris)

# distribution of train
print(table(tt$train$Species))

# distribution of test
print(table(tt$test$Species))

    setosa versicolor  virginica 
        40         40         40 

    setosa versicolor  virginica 
        10         10         10

Dividing a dataset into folds¶

In [4]:

#using stratified sampling
# preparing dataset into four folds
sample <- sample_stratified("Species")
folds <- k_fold(sample, iris, 4)

# distribution of folds
tbl <- NULL
for (f in folds) {
tbl <- rbind(tbl, table(f$Species))
}
print(tbl)

     setosa versicolor virginica
[1,]     13         13        13
[2,]     13         13        13
[3,]     12         12        12
[4,]     12         12        12

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