48 lines
1.6 KiB
R
48 lines
1.6 KiB
R
require(xgboost)
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# load in the agaricus dataset
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
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nround <- 2
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param <- list(max.depth=2,eta=1,silent=1,objective='binary:logistic')
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cat('running cross validation\n')
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# do cross validation, this will print result out as
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# [iteration] metric_name:mean_value+std_value
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# std_value is standard deviation of the metric
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xgb.cv(param, dtrain, nround, nfold=5, metrics={'error'})
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cat('running cross validation, disable standard deviation display\n')
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# do cross validation, this will print result out as
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# [iteration] metric_name:mean_value+std_value
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# std_value is standard deviation of the metric
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xgb.cv(param, dtrain, nround, nfold=5,
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metrics={'error'}, , showsd = FALSE)
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###
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# you can also do cross validation with cutomized loss function
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# See custom_objective.R
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##
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print ('running cross validation, with cutomsized loss function')
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logregobj <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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preds <- 1/(1 + exp(-preds))
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grad <- preds - labels
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hess <- preds * (1 - preds)
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return(list(grad = grad, hess = hess))
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}
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evalerror <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
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return(list(metric = "error", value = err))
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}
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param <- list(max.depth=2,eta=1,silent=1)
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# train with customized objective
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xgb.cv(param, dtrain, nround, nfold = 5,
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obj = logregobj, feval=evalerror)
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