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→Примеры алгоритмов: gbm added
#printing parameters
print(fit)
</pre>
=== GBM ===
<pre>
#loading libraries
install.packages("mlr")
library(mlr)
#loading data
train <- read.csv("input.csv")
test <- read.csv("testInput.csv")
#loading GBM
getParamSet("classif.gbm")
baseLearner <- makeLearner("classif.gbm", predict.type = "response")
#specifying parameters
controlFunction <- makeTuneControlRandom(maxit = 50000)#specifying tuning method
cvFunction <- makeResampleDesc("CV",iters = 100000) #definig cross-validation function
gbmParameters<- makeParamSet(
makeDiscreteParam("distribution", values = "bernoulli"),
makeIntegerParam("n.trees", lower = 100, upper = 1000), #number of trees
makeIntegerParam("interaction.depth", lower = 2, upper = 10), #depth of tree
makeIntegerParam("n.minobsinnode", lower = 10, upper = 80),
makeNumericParam("shrinkage",lower = 0.01, upper = 1)
)
#tunning parameters
gbmTuningParameters <- tuneParams(learner = baseLearner, task = trainTask,resampling = cvFunction,measures = acc,par.set = gbmParameters,control = controlFunction)
#creating model parameters
model <- setHyperPars(learner = baseLearner, par.vals = gbmTuningParameters)
#evaluating model
fit <- train(model, train)
predictions <- predict(fit, test)
</pre>
