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)Task]
The task.character(1)]
The method used to compute the feature importance.
The only method available is “permutation.importance”.
Default is “permutation.importance”.Learner | character(1)]
The learner.
If you pass a string the learner will be created via makeLearner.character]
The features to compute the importance of.
The default is all of the features contained in the Task.logical(1)]
Whether to compute the importance of the features argument jointly.
For method = "permutation.importance" this entails permuting the values of
all features together and then contrasting the performance with that of
the performance without the features being permuted.
The default is FALSE.Measure]
Performance measure.
Default is the first measure used in the benchmark experiment.function]
A difference function that takes a numeric vector and returns a numeric vector
of the same length.
The default is element-wise difference between the vectors.function]
A function which aggregates the differences.
This function must take a numeric vector and return a numeric vector of length 1.
The default is mean.integer(1)]
The number of Monte-Carlo iterations to use in computing the feature importance.
If nmc == -1 and method = "permutation.importance" then all
permutations of the features are used.
The default is 50.logical(1)]
Whether or not to sample the feature values with or without replacement.
The default is TRUE.logical(1)]
Whether to compute the per-observation importance.
The default is FALSE.FeatureImportance]. A named list which contains the computed feature importance and the input arguments. Object members:
data.frame]
Has columns for each feature or combination of features (colon separated) for which the importance is computed.
A row coresponds to importance of the feature specified in the column for the target.
logical(1)]
Whether or not the importance of the features was computed jointly rather than individually.
Measure]function]
The function used to compare the performance of predictions.
function]
The function which is used to aggregate the contrast between the performance of predictions across Monte-Carlo iterations.
logical(1)]
Whether or not, when method = "permutation.importance", the feature values
are sampled with replacement.
integer(1)]
The number of Monte-Carlo iterations used to compute the feature importance.
When nmc == -1 and method = "permutation.importance" all permutations are used.
logical(1)]
Whether observation-specific importance is computed for the features.
generateCalibrationData,
generateCritDifferencesData,
generateFilterValuesData,
generateFunctionalANOVAData,
generateLearningCurveData,
generatePartialDependenceData,
generateThreshVsPerfData,
getFilterValues,
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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