Description Usage Arguments Value See Also Examples

Set threshold of prediction object for classification or multilabel classification.
Creates corresponding discrete class response for the newly set threshold.
For binary classification: The positive class is predicted if the probability value exceeds the threshold.
For multiclass: Probabilities are divided by corresponding thresholds and the class with maximum resulting value is selected.
The result of both are equivalent if in the multi-threshold case the values are greater than 0 and sum to 1.
For multilabel classification: A label is predicted (with entry `TRUE`

) if a probability matrix entry
exceeds the threshold of the corresponding label.

1 | ```
setThreshold(pred, threshold)
``` |

`pred` |
[ |

`threshold` |
[ |

[`Prediction`

] with changed threshold and corresponding response.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# create task and train learner (LDA)
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.lda", predict.type = "prob")
mod = train(lrn, task)
# predict probabilities and compute performance
pred = predict(mod, newdata = iris)
performance(pred, measures = mmce)
head(as.data.frame(pred))
# adjust threshold and predict probabilities again
threshold = c(setosa = 0.4, versicolor = 0.3, virginica = 0.3)
pred = setThreshold(pred, threshold = threshold)
performance(pred, measures = mmce)
head(as.data.frame(pred))
``` |

guillermozbta/s2 documentation built on Jan. 2, 2018, 12:25 a.m.

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