View source: R/generateFeatureImportance.R
generateFeatureImportanceData | R Documentation |
Estimate how important individual features or groups of features are by contrasting prediction performances. For method “permutation.importance” compute the change in performance from permuting the values of a feature (or a group of features) and compare that to the predictions made on the unmcuted data.
generateFeatureImportanceData( task, method = "permutation.importance", learner, features = getTaskFeatureNames(task), interaction = FALSE, measure, contrast = function(x, y) x - y, aggregation = mean, nmc = 50L, replace = TRUE, local = FALSE, show.info = FALSE )
task |
(Task) |
method |
( |
learner |
(Learner | |
features |
(character) |
interaction |
( |
measure |
(Measure) |
contrast |
( |
aggregation |
( |
nmc |
( |
replace |
( |
local |
( |
show.info |
( |
(FeatureImportance
). A named list which contains the computed feature importance and the input arguments.
Object members:
res |
(data.frame) |
interaction |
( |
measure |
(Measure) |
The measure used to compute performance.
contrast |
( |
aggregation |
( |
replace |
( |
nmc |
( |
local |
( |
Jerome Friedman; Greedy Function Approximation: A Gradient Boosting Machine, Annals of Statistics, Vol. 29, No. 5 (Oct., 2001), pp. 1189-1232.
Other generate_plot_data:
generateCalibrationData()
,
generateCritDifferencesData()
,
generateFilterValuesData()
,
generateLearningCurveData()
,
generatePartialDependenceData()
,
generateThreshVsPerfData()
,
plotFilterValues()
lrn = makeLearner("classif.rpart", predict.type = "prob") fit = train(lrn, iris.task) imp = generateFeatureImportanceData(iris.task, "permutation.importance", lrn, "Petal.Width", nmc = 10L, local = TRUE)
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