Create A Graph to Perform "One vs. Rest" classification.

Create a new Graph for a classification Task to perform "One vs. Rest" classification.

All input arguments are cloned and have no references in common with the returned Graph.

Usage

pipeline_ovr(graph)

Arguments

graph

Graph
Graph being wrapped between PipeOpOVRSplit and PipeOpOVRUnite. The Graph should return NULL during training and a classification Prediction during prediction.

Value

Graph

Examples

library("mlr3")

task = tsk("wine")

learner = lrn("classif.rpart")
learner$predict_type = "prob"

# Simple OVR
g1 = pipeline_ovr(learner)
g1$train(task)
#> $ovrunite.output
#> NULL
#> 
g1$predict(task)
#> $ovrunite.output
#> 
#> ── <PredictionClassif> for 178 observations: ───────────────────────────────────
#>  row_ids truth response     prob.1     prob.2     prob.3
#>        1     1        1 0.93301153 0.04437722 0.02261125
#>        2     1        1 0.93301153 0.04437722 0.02261125
#>        3     1        1 0.93301153 0.04437722 0.02261125
#>      ---   ---      ---        ---        ---        ---
#>      176     3        3 0.08896784 0.04157376 0.86945841
#>      177     3        3 0.08896784 0.04157376 0.86945841
#>      178     3        3 0.01729144 0.04484461 0.93786395
#> 

# Bagged Learners
gr = po("replicate", reps = 3) %>>%
  po("subsample") %>>%
  learner %>>%
  po("classifavg", collect_multiplicity = TRUE)
g2 = pipeline_ovr(gr)
g2$train(task)
#> $ovrunite.output
#> NULL
#> 
g2$predict(task)
#> $ovrunite.output
#> 
#> ── <PredictionClassif> for 178 observations: ───────────────────────────────────
#>  row_ids truth response     prob.1     prob.2     prob.3
#>        1     1        1 0.94197522 0.04966703 0.00835775
#>        2     1        1 0.94197522 0.04966703 0.00835775
#>        3     1        1 0.94197522 0.04966703 0.00835775
#>      ---   ---      ---        ---        ---        ---
#>      176     3        3 0.25562834 0.03620269 0.70816897
#>      177     3        3 0.25562834 0.03620269 0.70816897
#>      178     3        3 0.03414673 0.04697450 0.91887877
#> 

# Bagging outside OVR
g3 = po("replicate", reps = 3) %>>%
  pipeline_ovr(po("subsample") %>>% learner) %>>%
  po("classifavg", collect_multiplicity = TRUE)
g3$train(task)
#> $classifavg.output
#> NULL
#> 
g3$predict(task)
#> $classifavg.output
#> 
#> ── <PredictionClassif> for 178 observations: ───────────────────────────────────
#>  row_ids truth response      prob.1     prob.2    prob.3
#>        1     1        1 0.955836274 0.04416373 0.0000000
#>        2     1        1 0.955836274 0.04416373 0.0000000
#>        3     1        1 0.955836274 0.04416373 0.0000000
#>      ---   ---      ---         ---        ---       ---
#>      176     3        3 0.078403407 0.04290101 0.8786956
#>      177     3        3 0.078403407 0.04290101 0.8786956
#>      178     3        3 0.004406163 0.04519203 0.9504018
#>