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################################################################################ | |
## Decision forest modeling | |
################################################################################ | |
#Decision Forest | |
#using rxDForest() to build ML model | |
DForest_model <- rxDForest(formula = formula, | |
data = trainingdata, | |
seed = 10, | |
cp = 0.01, | |
nTree = 50, | |
mTry = 2, | |
overwrite = TRUE, | |
reportProgress = 0) | |
DForest_model | |
class(DForest_model) #"rxDForest" | |
################################################################################ | |
## Boosted tree modeling | |
################################################################################ | |
BoostedTree_model = rxBTrees(formula = formula, | |
data = trainingdata, | |
learningRate = 0.2, | |
minSplit = 10, | |
minBucket = 10, | |
nTree = 100, | |
lossFunction = "multinomial", | |
reportProgress = 0) | |
BoostedTree_model | |
class(BoostedTree_model) | |
################################################################################ | |
## Decision Tree Modelling | |
################################################################################ | |
#rxDTree | |
DTree_model = rxDTree(formula = formula, | |
data = trainingdata, | |
minSplit = 10, | |
minBucket = 10, | |
nTree = 100, | |
reportProgress = 0) | |
DTree_model | |
class(DTree_model) | |
################################################################################ |
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