createDataPartition: Data Splitting functions
In caret: Classification and Regression Training
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
A series of test/training partitions are created using
createDataPartition while createResample creates one or
more bootstrap samples. createFolds splits the data into
k groups while createTimeSlices creates cross-validation
sample information to be used with time series data.
Usage
1
2
3
4
5
6
7
8
9
createDataPartition(y,
times = 1,
p = 0.5,
list = TRUE,
groups = min(5, length(y)))
createResample(y, times = 10, list = TRUE)
createFolds(y, k = 10, list = TRUE, returnTrain = FALSE)
createMultiFolds(y, k = 10, times = 5)
createTimeSlices(y, initialWindow, horizon = 1, fixedWindow = TRUE)
Arguments
y
a vector of outcomes. For createTimeSlices, these should be in chronological order.
times
the number of partitions to create
p
the percentage of data that goes to training
list
logical - should the results be in a list (TRUE) or a matrix
with the number of rows equal to floor(p * length(y)) and times
columns.
groups
for numeric y, the number of breaks in the quantiles
(see below)
k
an integer for the number of folds.
returnTrain
a logical. When true, the values returned are the
sample positions corresponding to the data used during
training. This argument only works in conjunction with list = TRUE
initialWindow
The initial number of consecutive values in each training set sample
horizon
The number of consecutive values in test set sample
fixedWindow
A logical: if FALSE, the training set always start at the first sample.
Details
For bootstrap samples, simple random sampling is used.
For other data splitting, the random sampling is done within the
levels of y when y is a factor in an attempt to balance
the class distributions within the splits.
For numeric y, the sample is split into groups sections based
on percentiles and sampling is done within these subgroups. For
createDataPartition, the number of percentiles is set via the
groups argument. For createFolds and createMultiFolds,
the number of groups is set dynamically based on the sample size and k.
For smaller samples sizes, these two functions may not do stratified
splitting and, at most, will split the data into quartiles.
Also, for createDataPartition, very small class sizes (<= 3) the
classes may not show up in both the training and test data
For multiple k-fold cross-validation, completely independent folds are created.
The names of the list objects will denote the fold membership using the pattern
"Foldi.Repj" meaning the ith section (of k) of the jth cross-validation set
(of times). Note that this function calls createFolds with
list = TRUE and returnTrain = TRUE.
Hyndman and Athanasopoulos (2013)) discuss rolling forecasting origin< techniques that move the training and test sets in time. createTimeSlices can create the indices for this type of splitting.
Value
A list or matrix of row position integers corresponding to the training
data
Author(s)
Max Kuhn, createTimeSlices by Tony Cooper
References
http://caret.r-forge.r-project.org/splitting.html
Hyndman and Athanasopoulos (2013), Forecasting: principles and practice. https://www.otexts.org/fpp
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
data(oil)
createDataPartition(oilType, 2)
x <- rgamma(50, 3, .5)
inA <- createDataPartition(x, list = FALSE)
plot(density(x[inA]))
rug(x[inA])
points(density(x[-inA]), type = "l", col = 4)
rug(x[-inA], col = 4)
createResample(oilType, 2)
createFolds(oilType, 10)
createFolds(oilType, 5, FALSE)
createFolds(rnorm(21))
createTimeSlices(1:9, 5, 1, fixedWindow = FALSE)
createTimeSlices(1:9, 5, 1, fixedWindow = TRUE)
createTimeSlices(1:9, 5, 3, fixedWindow = TRUE)
createTimeSlices(1:9, 5, 3, fixedWindow = FALSE)
Example output
Loading required package: lattice
Loading required package: ggplot2
$Resample1
[1] 1 10 11 12 13 14 15 16 22 23 24 25 26 27 30 31 36 37 39 40 41 47 48 50 52
[26] 53 54 55 59 60 61 62 66 70 72 73 74 75 77 78 79 80 81 83 86 89 90 91 93 96
$Resample2
[1] 3 4 9 10 11 13 14 15 16 19 22 23 24 26 27 28 29 32 34 37 38 39 40 42 44
[26] 45 48 50 51 54 55 56 57 60 61 63 64 65 68 69 70 76 77 80 81 85 87 88 91 94
$Resample1
[1] 1 1 2 2 4 5 7 7 7 9 10 10 12 13 13 13 14 15 17 18 19 20 21 23 24
[26] 24 25 29 29 32 33 34 34 35 36 36 38 38 40 40 44 46 49 50 50 50 51 52 53 53
[51] 54 55 56 56 57 58 59 61 61 62 63 64 66 66 66 67 69 70 71 71 71 74 74 74 75
[76] 76 76 76 76 77 78 78 78 79 80 81 81 82 85 87 89 91 92 92 94 95
$Resample2
[1] 2 4 5 5 7 8 8 8 9 10 11 11 12 13 14 15 15 16 16 17 19 20 21 22 22
[26] 23 27 28 29 32 33 34 34 34 34 35 35 37 37 38 39 40 41 42 42 43 44 45 46 48
[51] 48 48 51 52 56 57 58 59 60 61 62 62 62 62 62 64 66 66 67 68 68 69 70 70 71
[76] 72 73 74 75 76 77 77 83 87 87 89 90 90 91 92 93 93 94 94 94 94
$Fold01
[1] 12 14 29 35 36 44 53 59 68 76
$Fold02
[1] 1 9 15 22 54 57 80 96
$Fold03
[1] 18 27 46 60 63 64 72 77 84 89
$Fold04
[1] 17 33 37 49 52 67 87 95
$Fold05
[1] 3 7 20 24 40 41 42 47 86 92 94
$Fold06
[1] 4 11 23 32 48 56 61 71 73 88 90
$Fold07
[1] 10 16 19 34 39 51 70 81 91
$Fold08
[1] 5 6 13 30 31 43 50 62 75 83 93
$Fold09
[1] 8 25 28 66 74 79 82
$Fold10
[1] 2 21 26 38 45 55 58 65 69 78 85
[1] 4 2 5 3 5 4 2 5 5 4 4 1 2 2 1 2 2 5 5 2 1 4 1 5 4 5 1 4 5 1 5 5 2 4 1 2 3 4
[39] 5 2 3 1 5 5 1 1 1 4 2 2 3 1 2 4 5 3 5 1 5 3 2 4 4 4 1 3 4 1 3 5 1 3 3 1 1 3
[77] 2 5 3 3 1 2 4 4 3 4 1 3 2 1 2 5 4 3 2 3
$Fold01
[1] 3 7
$Fold02
[1] 8 10
$Fold03
[1] 16 20
$Fold04
[1] 2 13 17
$Fold05
[1] 4 12
$Fold06
[1] 11 21
$Fold07
[1] 5 19
$Fold08
[1] 1 15
$Fold09
[1] 6 9
$Fold10
[1] 14 18
$train
$train$Training5
[1] 1 2 3 4 5
$train$Training6
[1] 1 2 3 4 5 6
$train$Training7
[1] 1 2 3 4 5 6 7
$train$Training8
[1] 1 2 3 4 5 6 7 8
$test
$test$Testing5
[1] 6
$test$Testing6
[1] 7
$test$Testing7
[1] 8
$test$Testing8
[1] 9
$train
$train$Training5
[1] 1 2 3 4 5
$train$Training6
[1] 2 3 4 5 6
$train$Training7
[1] 3 4 5 6 7
$train$Training8
[1] 4 5 6 7 8
$test
$test$Testing5
[1] 6
$test$Testing6
[1] 7
$test$Testing7
[1] 8
$test$Testing8
[1] 9
$train
$train$Training5
[1] 1 2 3 4 5
$train$Training6
[1] 2 3 4 5 6
$test
$test$Testing5
[1] 6 7 8
$test$Testing6
[1] 7 8 9
$train
$train$Training5
[1] 1 2 3 4 5
$train$Training6
[1] 1 2 3 4 5 6
$test
$test$Testing5
[1] 6 7 8
$test$Testing6
[1] 7 8 9
caret documentation built on May 2, 2019, 5:47 p.m.
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Description Usage Arguments Details Value Author(s) References Examples
Description
A series of test/training partitions are created using
createDataPartition while createResample creates one or
more bootstrap samples. createFolds splits the data into
k groups while createTimeSlices creates cross-validation
sample information to be used with time series data.
Usage
1 2 3 4 5 6 7 8 9 | createDataPartition(y,
times = 1,
p = 0.5,
list = TRUE,
groups = min(5, length(y)))
createResample(y, times = 10, list = TRUE)
createFolds(y, k = 10, list = TRUE, returnTrain = FALSE)
createMultiFolds(y, k = 10, times = 5)
createTimeSlices(y, initialWindow, horizon = 1, fixedWindow = TRUE)
|
Arguments
y |
a vector of outcomes. For |
times |
the number of partitions to create |
p |
the percentage of data that goes to training |
list |
logical - should the results be in a list ( |
groups |
for numeric |
k |
an integer for the number of folds. |
returnTrain |
a logical. When true, the values returned are the
sample positions corresponding to the data used during
training. This argument only works in conjunction with |
initialWindow |
The initial number of consecutive values in each training set sample |
horizon |
The number of consecutive values in test set sample |
fixedWindow |
A logical: if |
Details
For bootstrap samples, simple random sampling is used.
For other data splitting, the random sampling is done within the
levels of y when y is a factor in an attempt to balance
the class distributions within the splits.
For numeric y, the sample is split into groups sections based
on percentiles and sampling is done within these subgroups. For
createDataPartition, the number of percentiles is set via the
groups argument. For createFolds and createMultiFolds,
the number of groups is set dynamically based on the sample size and k.
For smaller samples sizes, these two functions may not do stratified
splitting and, at most, will split the data into quartiles.
Also, for createDataPartition, very small class sizes (<= 3) the
classes may not show up in both the training and test data
For multiple k-fold cross-validation, completely independent folds are created.
The names of the list objects will denote the fold membership using the pattern
"Foldi.Repj" meaning the ith section (of k) of the jth cross-validation set
(of times). Note that this function calls createFolds with
list = TRUE and returnTrain = TRUE.
Hyndman and Athanasopoulos (2013)) discuss rolling forecasting origin< techniques that move the training and test sets in time. createTimeSlices can create the indices for this type of splitting.
Value
A list or matrix of row position integers corresponding to the training data
Author(s)
Max Kuhn, createTimeSlices by Tony Cooper
References
http://caret.r-forge.r-project.org/splitting.html
Hyndman and Athanasopoulos (2013), Forecasting: principles and practice. https://www.otexts.org/fpp
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | data(oil)
createDataPartition(oilType, 2)
x <- rgamma(50, 3, .5)
inA <- createDataPartition(x, list = FALSE)
plot(density(x[inA]))
rug(x[inA])
points(density(x[-inA]), type = "l", col = 4)
rug(x[-inA], col = 4)
createResample(oilType, 2)
createFolds(oilType, 10)
createFolds(oilType, 5, FALSE)
createFolds(rnorm(21))
createTimeSlices(1:9, 5, 1, fixedWindow = FALSE)
createTimeSlices(1:9, 5, 1, fixedWindow = TRUE)
createTimeSlices(1:9, 5, 3, fixedWindow = TRUE)
createTimeSlices(1:9, 5, 3, fixedWindow = FALSE)
|
Example output
Loading required package: lattice
Loading required package: ggplot2
$Resample1
[1] 1 10 11 12 13 14 15 16 22 23 24 25 26 27 30 31 36 37 39 40 41 47 48 50 52
[26] 53 54 55 59 60 61 62 66 70 72 73 74 75 77 78 79 80 81 83 86 89 90 91 93 96
$Resample2
[1] 3 4 9 10 11 13 14 15 16 19 22 23 24 26 27 28 29 32 34 37 38 39 40 42 44
[26] 45 48 50 51 54 55 56 57 60 61 63 64 65 68 69 70 76 77 80 81 85 87 88 91 94
$Resample1
[1] 1 1 2 2 4 5 7 7 7 9 10 10 12 13 13 13 14 15 17 18 19 20 21 23 24
[26] 24 25 29 29 32 33 34 34 35 36 36 38 38 40 40 44 46 49 50 50 50 51 52 53 53
[51] 54 55 56 56 57 58 59 61 61 62 63 64 66 66 66 67 69 70 71 71 71 74 74 74 75
[76] 76 76 76 76 77 78 78 78 79 80 81 81 82 85 87 89 91 92 92 94 95
$Resample2
[1] 2 4 5 5 7 8 8 8 9 10 11 11 12 13 14 15 15 16 16 17 19 20 21 22 22
[26] 23 27 28 29 32 33 34 34 34 34 35 35 37 37 38 39 40 41 42 42 43 44 45 46 48
[51] 48 48 51 52 56 57 58 59 60 61 62 62 62 62 62 64 66 66 67 68 68 69 70 70 71
[76] 72 73 74 75 76 77 77 83 87 87 89 90 90 91 92 93 93 94 94 94 94
$Fold01
[1] 12 14 29 35 36 44 53 59 68 76
$Fold02
[1] 1 9 15 22 54 57 80 96
$Fold03
[1] 18 27 46 60 63 64 72 77 84 89
$Fold04
[1] 17 33 37 49 52 67 87 95
$Fold05
[1] 3 7 20 24 40 41 42 47 86 92 94
$Fold06
[1] 4 11 23 32 48 56 61 71 73 88 90
$Fold07
[1] 10 16 19 34 39 51 70 81 91
$Fold08
[1] 5 6 13 30 31 43 50 62 75 83 93
$Fold09
[1] 8 25 28 66 74 79 82
$Fold10
[1] 2 21 26 38 45 55 58 65 69 78 85
[1] 4 2 5 3 5 4 2 5 5 4 4 1 2 2 1 2 2 5 5 2 1 4 1 5 4 5 1 4 5 1 5 5 2 4 1 2 3 4
[39] 5 2 3 1 5 5 1 1 1 4 2 2 3 1 2 4 5 3 5 1 5 3 2 4 4 4 1 3 4 1 3 5 1 3 3 1 1 3
[77] 2 5 3 3 1 2 4 4 3 4 1 3 2 1 2 5 4 3 2 3
$Fold01
[1] 3 7
$Fold02
[1] 8 10
$Fold03
[1] 16 20
$Fold04
[1] 2 13 17
$Fold05
[1] 4 12
$Fold06
[1] 11 21
$Fold07
[1] 5 19
$Fold08
[1] 1 15
$Fold09
[1] 6 9
$Fold10
[1] 14 18
$train
$train$Training5
[1] 1 2 3 4 5
$train$Training6
[1] 1 2 3 4 5 6
$train$Training7
[1] 1 2 3 4 5 6 7
$train$Training8
[1] 1 2 3 4 5 6 7 8
$test
$test$Testing5
[1] 6
$test$Testing6
[1] 7
$test$Testing7
[1] 8
$test$Testing8
[1] 9
$train
$train$Training5
[1] 1 2 3 4 5
$train$Training6
[1] 2 3 4 5 6
$train$Training7
[1] 3 4 5 6 7
$train$Training8
[1] 4 5 6 7 8
$test
$test$Testing5
[1] 6
$test$Testing6
[1] 7
$test$Testing7
[1] 8
$test$Testing8
[1] 9
$train
$train$Training5
[1] 1 2 3 4 5
$train$Training6
[1] 2 3 4 5 6
$test
$test$Testing5
[1] 6 7 8
$test$Testing6
[1] 7 8 9
$train
$train$Training5
[1] 1 2 3 4 5
$train$Training6
[1] 1 2 3 4 5 6
$test
$test$Testing5
[1] 6 7 8
$test$Testing6
[1] 7 8 9
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