FailureModel: Failure model.
In berndbischl/mlr: Machine Learning in R
FailureModel R Documentation
Failure model.
Description
A subclass of WrappedModel. It is created
if you set the respective option in configureMlr -
when a model internally crashed during training.
The model always predicts NAs.
The if mlr option on.error.dump is TRUE, the
FailureModel contains the debug trace of the error.
It can be accessed with getFailureModelDump and
inspected with debugger.
Its encapsulated learner.model is simply a string:
The error message that was generated when the model crashed.
The following code shows how to access the message.
See Also
Other debug:
ResampleResult,
getPredictionDump(),
getRRDump()
Examples
configureMlr(on.learner.error = "warn")
data = iris
data$newfeat = 1 # will make LDA crash
task = makeClassifTask(data = data, target = "Species")
m = train("classif.lda", task) # LDA crashed, but mlr catches this
print(m)
print(m$learner.model) # the error message
p = predict(m, task) # this will predict NAs
print(p)
print(performance(p))
configureMlr(on.learner.error = "stop")
berndbischl/mlr documentation built on Aug. 15, 2024, 4:20 p.m.
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| FailureModel | R Documentation |
Failure model.
Description
A subclass of WrappedModel. It is created
if you set the respective option in configureMlr - when a model internally crashed during training. The model always predicts NAs.
The if mlr option on.error.dump is TRUE, the
FailureModel contains the debug trace of the error.
It can be accessed with getFailureModelDump and
inspected with debugger.
Its encapsulated learner.model is simply a string:
The error message that was generated when the model crashed.
The following code shows how to access the message.
See Also
Other debug:
ResampleResult,
getPredictionDump(),
getRRDump()
Examples
configureMlr(on.learner.error = "warn")
data = iris
data$newfeat = 1 # will make LDA crash
task = makeClassifTask(data = data, target = "Species")
m = train("classif.lda", task) # LDA crashed, but mlr catches this
print(m)
print(m$learner.model) # the error message
p = predict(m, task) # this will predict NAs
print(p)
print(performance(p))
configureMlr(on.learner.error = "stop")
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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