crossvaldata: Computes k-fold cross validation for rminer models.

In rminer: Data Mining Classification and Regression Methods

Description

Computes k-fold cross validation for rminer models.

Usage

crossvaldata(x, data, theta.fit, theta.predict, ngroup = 10, 
             mode = "stratified", seed = NULL, model, task, feature = "none",
             ...)

Arguments

x	See fit for details.
data	See fit for details.
theta.fit	fitting function
theta.predict	prediction function
ngroup	number of folds
mode	Possibilities are: "stratified", "random" or "order" (see holdout for details).
seed	if NULL then no seed is used and the current R randomness is assumed; else a fixed seed is adopted to generate local random sample sequences, returning always the same result for the same seed (local means that it does not affect the state of other random number generations called after this function, see holdout example).
model	See fit for details.
task	See fit for details.
feature	See fit for details.
...	Additional parameters sent to theta.fit or theta.predic (e.g. search)

Details

Standard k-fold cross-validation adopted for rminer models. By default, for classification tasks ("class" or "prob") a stratified sampling is used (the class distributions are identical for each fold), unless mode is set to random or order (see holdout for details).

Value

Returns a list with:

• $cv.fit – all predictions (factor if task="class", matrix if task="prob" or numeric if task="reg");

• $model – vector list with the model for each fold.

• $mpar – vector list with the mpar for each fold;

• $attributes – the selected attributes for each fold if a feature selection algorithm was adopted;

• $ngroup – the number of folds;

• $leave.out – the computed size for each fold (=nrow(data)/ngroup);

• $groups – vector list with the indexes of each group;

• $call – the call of this function;

Note

A better control (e.g. use of several Runs) is achieved using the simpler mining function.

Author(s)

This function was adapted by Paulo Cortez from the crossval function of the bootstrap library (S original by R. Tibshirani and R port by F. Leisch).

References

Check the crossval function of the bootstrap library.

See Also

holdout, fit, mining and predict.fit.

Examples

### dontrun is used when the execution of the example requires some computational effort.
## Not run: 
 data(iris)
 # 3-fold cross validation using fit and predict
 # the control argument is sent to rpart function 
 # rpart.control() is from the rpart package
 M=crossvaldata(Species~.,iris,fit,predict,ngroup=3,seed=12345,model="rpart",
                task="prob", control = rpart::rpart.control(cp=0.05))
 print("cross validation object:")
 print(M)
 C=mmetric(iris$Species,M$cv.fit,metric="CONF")
 print("confusion matrix:")
 print(C)

## End(Not run)

Example output

[1] "cross validation object:"
$cv.fit
       [,1]       [,2]       [,3]
  [1,]    1 0.00000000 0.00000000
  [2,]    1 0.00000000 0.00000000
  [3,]    1 0.00000000 0.00000000
  [4,]    1 0.00000000 0.00000000
  [5,]    1 0.00000000 0.00000000
  [6,]    1 0.00000000 0.00000000
  [7,]    1 0.00000000 0.00000000
  [8,]    1 0.00000000 0.00000000
  [9,]    1 0.00000000 0.00000000
 [10,]    1 0.00000000 0.00000000
 [11,]    1 0.00000000 0.00000000
 [12,]    1 0.00000000 0.00000000
 [13,]    1 0.00000000 0.00000000
 [14,]    1 0.00000000 0.00000000
 [15,]    1 0.00000000 0.00000000
 [16,]    1 0.00000000 0.00000000
 [17,]    1 0.00000000 0.00000000
 [18,]    1 0.00000000 0.00000000
 [19,]    1 0.00000000 0.00000000
 [20,]    1 0.00000000 0.00000000
 [21,]    1 0.00000000 0.00000000
 [22,]    1 0.00000000 0.00000000
 [23,]    1 0.00000000 0.00000000
 [24,]    1 0.00000000 0.00000000
 [25,]    1 0.00000000 0.00000000
 [26,]    1 0.00000000 0.00000000
 [27,]    1 0.00000000 0.00000000
 [28,]    1 0.00000000 0.00000000
 [29,]    1 0.00000000 0.00000000
 [30,]    1 0.00000000 0.00000000
 [31,]    1 0.00000000 0.00000000
 [32,]    1 0.00000000 0.00000000
 [33,]    1 0.00000000 0.00000000
 [34,]    1 0.00000000 0.00000000
 [35,]    1 0.00000000 0.00000000
 [36,]    1 0.00000000 0.00000000
 [37,]    1 0.00000000 0.00000000
 [38,]    1 0.00000000 0.00000000
 [39,]    1 0.00000000 0.00000000
 [40,]    1 0.00000000 0.00000000
 [41,]    1 0.00000000 0.00000000
 [42,]    1 0.00000000 0.00000000
 [43,]    1 0.00000000 0.00000000
 [44,]    1 0.00000000 0.00000000
 [45,]    1 0.00000000 0.00000000
 [46,]    1 0.00000000 0.00000000
 [47,]    1 0.00000000 0.00000000
 [48,]    1 0.00000000 0.00000000
 [49,]    1 0.00000000 0.00000000
 [50,]    1 0.00000000 0.00000000
 [51,]    0 0.89189189 0.10810811
 [52,]    0 0.97142857 0.02857143
 [53,]    0 0.97142857 0.02857143
 [54,]    0 0.94117647 0.05882353
 [55,]    0 0.94117647 0.05882353
 [56,]    0 0.94117647 0.05882353
 [57,]    0 0.94117647 0.05882353
 [58,]    0 0.89189189 0.10810811
 [59,]    0 0.97142857 0.02857143
 [60,]    0 0.89189189 0.10810811
 [61,]    0 0.97142857 0.02857143
 [62,]    0 0.89189189 0.10810811
 [63,]    0 0.97142857 0.02857143
 [64,]    0 0.97142857 0.02857143
 [65,]    0 0.94117647 0.05882353
 [66,]    0 0.97142857 0.02857143
 [67,]    0 0.89189189 0.10810811
 [68,]    0 0.94117647 0.05882353
 [69,]    0 0.97142857 0.02857143
 [70,]    0 0.94117647 0.05882353
 [71,]    0 0.00000000 1.00000000
 [72,]    0 0.89189189 0.10810811
 [73,]    0 0.94117647 0.05882353
 [74,]    0 0.94117647 0.05882353
 [75,]    0 0.89189189 0.10810811
 [76,]    0 0.94117647 0.05882353
 [77,]    0 0.97142857 0.02857143
 [78,]    0 0.00000000 1.00000000
 [79,]    0 0.89189189 0.10810811
 [80,]    0 0.89189189 0.10810811
 [81,]    0 0.97142857 0.02857143
 [82,]    0 0.97142857 0.02857143
 [83,]    0 0.89189189 0.10810811
 [84,]    0 0.00000000 1.00000000
 [85,]    0 0.97142857 0.02857143
 [86,]    0 0.89189189 0.10810811
 [87,]    0 0.94117647 0.05882353
 [88,]    0 0.94117647 0.05882353
 [89,]    0 0.97142857 0.02857143
 [90,]    0 0.97142857 0.02857143
 [91,]    0 0.94117647 0.05882353
 [92,]    0 0.97142857 0.02857143
 [93,]    0 0.89189189 0.10810811
 [94,]    0 0.89189189 0.10810811
 [95,]    0 0.89189189 0.10810811
 [96,]    0 0.89189189 0.10810811
 [97,]    0 0.94117647 0.05882353
 [98,]    0 0.89189189 0.10810811
 [99,]    0 0.94117647 0.05882353
[100,]    0 0.94117647 0.05882353
[101,]    0 0.00000000 1.00000000
[102,]    0 0.00000000 1.00000000
[103,]    0 0.03225806 0.96774194
[104,]    0 0.03225806 0.96774194
[105,]    0 0.03225806 0.96774194
[106,]    0 0.03225806 0.96774194
[107,]    0 0.97142857 0.02857143
[108,]    0 0.00000000 1.00000000
[109,]    0 0.03225806 0.96774194
[110,]    0 0.03225806 0.96774194
[111,]    0 0.00000000 1.00000000
[112,]    0 0.00000000 1.00000000
[113,]    0 0.00000000 1.00000000
[114,]    0 0.00000000 1.00000000
[115,]    0 0.00000000 1.00000000
[116,]    0 0.03225806 0.96774194
[117,]    0 0.00000000 1.00000000
[118,]    0 0.00000000 1.00000000
[119,]    0 0.03225806 0.96774194
[120,]    0 0.89189189 0.10810811
[121,]    0 0.00000000 1.00000000
[122,]    0 0.94117647 0.05882353
[123,]    0 0.00000000 1.00000000
[124,]    0 0.94117647 0.05882353
[125,]    0 0.00000000 1.00000000
[126,]    0 0.00000000 1.00000000
[127,]    0 0.00000000 1.00000000
[128,]    0 0.94117647 0.05882353
[129,]    0 0.00000000 1.00000000
[130,]    0 0.97142857 0.02857143
[131,]    0 0.00000000 1.00000000
[132,]    0 0.00000000 1.00000000
[133,]    0 0.00000000 1.00000000
[134,]    0 0.97142857 0.02857143
[135,]    0 0.97142857 0.02857143
[136,]    0 0.03225806 0.96774194
[137,]    0 0.00000000 1.00000000
[138,]    0 0.00000000 1.00000000
[139,]    0 0.94117647 0.05882353
[140,]    0 0.00000000 1.00000000
[141,]    0 0.03225806 0.96774194
[142,]    0 0.03225806 0.96774194
[143,]    0 0.00000000 1.00000000
[144,]    0 0.00000000 1.00000000
[145,]    0 0.03225806 0.96774194
[146,]    0 0.03225806 0.96774194
[147,]    0 0.00000000 1.00000000
[148,]    0 0.03225806 0.96774194
[149,]    0 0.00000000 1.00000000
[150,]    0 0.03225806 0.96774194

$model
$model[[1]]
[1] "rpart"

$model[[2]]
[1] "rpart"

$model[[3]]
[1] "rpart"


$mpar
$mpar[[1]]
$mpar[[1]]$control
$mpar[[1]]$control$minsplit
[1] 20

$mpar[[1]]$control$minbucket
[1] 7

$mpar[[1]]$control$cp
[1] 0.05

$mpar[[1]]$control$maxcompete
[1] 4

$mpar[[1]]$control$maxsurrogate
[1] 5

$mpar[[1]]$control$usesurrogate
[1] 2

$mpar[[1]]$control$surrogatestyle
[1] 0

$mpar[[1]]$control$maxdepth
[1] 30

$mpar[[1]]$control$xval
[1] 10


$mpar[[1]]$method
[1] "class"


$mpar[[2]]
$mpar[[2]]$control
$mpar[[2]]$control$minsplit
[1] 20

$mpar[[2]]$control$minbucket
[1] 7

$mpar[[2]]$control$cp
[1] 0.05

$mpar[[2]]$control$maxcompete
[1] 4

$mpar[[2]]$control$maxsurrogate
[1] 5

$mpar[[2]]$control$usesurrogate
[1] 2

$mpar[[2]]$control$surrogatestyle
[1] 0

$mpar[[2]]$control$maxdepth
[1] 30

$mpar[[2]]$control$xval
[1] 10


$mpar[[2]]$method
[1] "class"


$mpar[[3]]
$mpar[[3]]$control
$mpar[[3]]$control$minsplit
[1] 20

$mpar[[3]]$control$minbucket
[1] 7

$mpar[[3]]$control$cp
[1] 0.05

$mpar[[3]]$control$maxcompete
[1] 4

$mpar[[3]]$control$maxsurrogate
[1] 5

$mpar[[3]]$control$usesurrogate
[1] 2

$mpar[[3]]$control$surrogatestyle
[1] 0

$mpar[[3]]$control$maxdepth
[1] 30

$mpar[[3]]$control$xval
[1] 10


$mpar[[3]]$method
[1] "class"



$sen
NULL

$sresponses
[1] FALSE

$attributes
NULL

$ngroup
[1] 3

$leave.out
[1] 50

$groups
$groups[[1]]
 [1] 142  51  58  93  75  96   2  86 146  38 103  94  10  40 141  30   1  72  12
[20]   3  14 106 119  16  80  62 145  98 116  60 105  32  25  36 104 150 136   9
[39]   5  79  83  17 109  37 148  67 110  13  95 120

$groups[[2]]
 [1]  49  74  56  91  34  44  35 143 149  55  23  26  97   7  15  46 132 112  68
[20]  11 100 101  99 147  87  42  88   4  73 128  57  65  48  78 108 131  18 122
[39] 138  54  76  31  70 125  20  84 124 133 139   6

$groups[[3]]
 [1]  90  47  53 135  59  52 137  29 118 115  28 111 102 117   8 130  64 123  21
[20]  71  77 140  24  81  19 121  39  63  33 113  45 107  50  27  61  85  82 129
[39]  41  43 144  66  92 134 126 114 127  69  22  89


[1] "confusion matrix:"
$res
NULL

$conf
            pred
target       setosa versicolor virginica
  setosa         50          0         0
  versicolor      0         47         3
  virginica       0          9        41

$roc
NULL

$lift
NULL

rminer documentation built on Aug. 28, 2020, 5:08 p.m.