Description Usage Arguments Value References See Also Examples
View source: R/generatePartialDependence.R
Decompose a learned prediction function as a sum of components estimated via partial dependence.
1 2 3 |
obj |
[ |
input |
[ |
features |
[ |
depth |
[ |
fun |
[ |
bounds |
[ |
resample |
[ |
fmin |
[ |
fmax |
[ |
gridsize |
[ |
... |
additional arguments to be passed to |
[FunctionalANOVAData
]. A named list, which contains the computed effects of the specified
depth amongst the features.
Object members:
data |
[ |
task.desc |
[ |
target |
The target feature for regression. |
features |
[ |
interaction |
[ |
Giles Hooker, “Discovering additive structure in black box functions.” Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining (2004): 575-580.
Other generate_plot_data: generateCalibrationData
,
generateCritDifferencesData
,
generateFeatureImportanceData
,
generateFilterValuesData
,
generateLearningCurveData
,
generatePartialDependenceData
,
generateThreshVsPerfData
,
getFilterValues
,
plotFilterValues
1 2 3 | fit = train("regr.rpart", bh.task)
fa = generateFunctionalANOVAData(fit, bh.task, c("lstat", "crim"), depth = 2L)
plotPartialDependence(fa)
|
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