## 5.3 Execution

With a Task, a Learner and Resampling object we can call resample() and create a ResampleResult object.

rr = resample(task, learner, resampling)
print(rr)
## <ResampleResult> of learner 'iris' on task 'classif.rpart' with 3 iterations
##     Measure Min. 1st Qu. Median Mean 3rd Qu. Max. Sd
##  classif.ce 0.06    0.06   0.06 0.06    0.06 0.06  0

Before we go into more detail, let’s change the resampling to a “3-fold cross-validation” to better illustrate what operations are possible with a ResampleResult.

resampling = mlr_resamplings$get("cv", param_vals = list(folds = 3L)) rr = resample(task, learner, resampling) print(rr) ## <ResampleResult> of learner 'iris' on task 'classif.rpart' with 3 iterations ## Measure Min. 1st Qu. Median Mean 3rd Qu. Max. Sd ## classif.ce 0.04 0.06 0.08 0.06667 0.08 0.08 0.02309 The following operations are supported with ResampleResult objects: • Extract the performance for the individual resampling iterations: rr$performance("classif.ce")
## [1] 0.04 0.08 0.08
• Extract and inspect the resampling splits:

rr$resampling ## <ResamplingCV> with 3 iterations ## Instantiated: TRUE ## Parameters: folds=3 ## ## Public: clone, duplicated_ids, format, hash, id, instance, ## instantiate, is_instantiated, iters, param_set, task_hash, test_set, ## train_set rr$resampling$iters ## [1] 3 rr$resampling$test_set(1) ## [1] 4 10 21 23 27 28 29 31 32 33 34 35 36 37 40 42 43 44 47 ## [20] 49 51 52 58 64 75 76 79 80 81 87 92 103 105 106 107 109 111 113 ## [39] 114 117 119 126 128 129 131 133 139 147 148 149 rr$resampling$train_set(3) ## [1] 4 10 21 23 27 28 29 31 32 33 34 35 36 37 40 42 43 44 ## [19] 47 49 51 52 58 64 75 76 79 80 81 87 92 103 105 106 107 109 ## [37] 111 113 114 117 119 126 128 129 131 133 139 147 148 149 1 2 3 5 ## [55] 6 9 11 12 13 14 15 17 20 30 38 39 45 48 54 55 57 59 ## [73] 61 65 66 69 71 72 77 82 84 88 94 95 97 98 102 104 112 115 ## [91] 118 120 123 124 130 132 135 136 138 143 • Retrieve the experiment of a specific iteration and inspect it: e = rr$experiment(iter = 1)
e\$model
## NULL