Description Usage Arguments References Examples
creation of a D2C object which preprocesses the list of DAGs and observations contained in sDAG and fits a Random Forest classifier
1 2 3 4 | ## S4 method for signature 'D2C'
initialize(.Object, sDAG, descr = new("D2C.descriptor"),
verbose = TRUE, ratioMissingNode = 0, ratioEdges = 1,
max.features = 20, goParallel = FALSE)
|
.Object |
: the D2C object |
sDAG |
: simulateDAG object |
descr |
: D2C.descriptor object containing the parameters of the descriptor |
verbose |
: if TRUE it prints the state of progress |
ratioMissingNode |
: percentage of existing nodes which are not considered. This is used to emulate latent variables. |
ratioEdges |
: percentage of existing edges which are added to the training set |
max.features |
: maximum number of features used by the Random Forest classifier randomForest. The features are selected by the importance returned by the function importance. |
goParallel |
: if TRUE it uses parallelism |
Gianluca Bontempi, Maxime Flauder (2014) From dependency to causality: a machine learning approach. Under submission
1 2 3 4 5 6 7 8 | |
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