refine style with max.depth
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@@ -10,7 +10,7 @@
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#' \item \code{binary:logistic} logistic regression for classification
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#' }
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#' \item \code{eta} step size of each boosting step
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#' \item \code{max_depth} maximum depth of the tree
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#' \item \code{max.depth} maximum depth of the tree
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#' \item \code{nthread} number of thread used in training, if not set, all threads are used
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#' }
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#'
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@@ -50,7 +50,7 @@
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#' data(agaricus.train, package='xgboost')
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#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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#' history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
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#' "max_depth"=3, "eta"=1, "objective"="binary:logistic")
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#' "max.depth"=3, "eta"=1, "objective"="binary:logistic")
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#' @export
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#'
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xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,
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@@ -10,7 +10,7 @@
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#' \item \code{binary:logistic} logistic regression for classification
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#' }
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#' \item \code{eta} step size of each boosting step
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#' \item \code{max_depth} maximum depth of the tree
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#' \item \code{max.depth} maximum depth of the tree
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#' \item \code{nthread} number of thread used in training, if not set, all threads are used
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#' }
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#'
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@@ -50,7 +50,7 @@
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#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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#' dtest <- dtrain
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#' watchlist <- list(eval = dtest, train = dtrain)
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#' param <- list(max_depth = 2, eta = 1, silent = 1)
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#' param <- list(max.depth = 2, eta = 1, silent = 1)
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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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@@ -14,7 +14,7 @@
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#' \item \code{binary:logistic} logistic regression for classification
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#' }
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#' \item \code{eta} step size of each boosting step
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#' \item \code{max_depth} maximum depth of the tree
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#' \item \code{max.depth} maximum depth of the tree
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#' \item \code{nthread} number of thread used in training, if not set, all threads are used
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#' }
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#'
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