问题
I'm trying to split the iris dataset into a training set and a test set. I used createDataPartition()
like this:
library(caret)
createDataPartition(iris$Species, p=0.1)
# [1] 12 22 26 41 42 57 63 79 89 93 114 117 134 137 142
createDataPartition(iris$Sepal.Length, p=0.1)
# [1] 1 27 44 46 54 68 72 77 83 84 93 99 104 109 117 132 134
I understand the first query. I have a vector of 0.1*150 elements (150 is the number of samples in the dataset). However, I should have the same vector on the second query but I am getting a vector of 17 elements instead of 15.
Any ideas as to why I get these results?
回答1:
Sepal.Length
is a numeric feature; from the online documentation:
For numeric
y
, the sample is split into groups sections based on percentiles and sampling is done within these subgroups. ForcreateDataPartition
, the number of percentiles is set via thegroups
argument.
groups
: for numericy
, the number of breaks in the quantiles
with default value:
groups = min(5, length(y)
)
Here is what happens in your case:
Since you do not specify groups
, it takes a value of min(5, 150) = 5
breaks; now, in that case, these breaks coincide with the natural quantiles, i.e. the minimum, the 1st quantile, the median, the 3rd quantile, and the maximum - which you can see from the summary
:
> summary(iris$Sepal.Length)
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.300 5.100 5.800 5.843 6.400 7.900
For numeric features, the function will take a percentage of p = 0.1
from each one of the (4) intervals defined by the above breaks (quantiles); let's see how many samples we have per such interval:
l1 = length(which(iris$Sepal.Length >= 4.3 & iris$Sepal.Length <= 5.1)) # 41
l2 = length(which(iris$Sepal.Length > 5.1 & iris$Sepal.Length <= 5.8)) # 39
l3 = length(which(iris$Sepal.Length > 5.8 & iris$Sepal.Length <= 6.4)) # 35
l4 = length(which(iris$Sepal.Length > 6.4 & iris$Sepal.Length <= 7.9)) # 35
Exactly how many samples will be returned from each interval? Here is the catch - according to line # 140 of the source code, it will be the ceiling of the product between the no. of samples and your p
; let's see what this should be in your case for p = 0.1
:
ceiling(l1*p) + ceiling(l2*p) + ceiling(l3*p) + ceiling(l4*p)
# 17
Bingo! :)
来源:https://stackoverflow.com/questions/46581379/r-caret-createdatapartition-returns-more-samples-than-expected