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fixed typos in basic_walkthrough demo

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This commit is contained in:
Yuan Tang 2015-08-10 20:35:10 -04:00
parent 18e1ddec3c
commit 3dd40b9f37
1 changed files with 5 additions and 5 deletions
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10
R-package/demo/basic_walkthrough.R
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@ -1,7 +1,7 @@
require(xgboost)
require(methods)
# we load in the agaricus dataset
# In this example, we are aiming to predict whether a mushroom can be eated
# In this example, we are aiming to predict whether a mushroom can be eaten
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
@ -12,8 +12,8 @@ class(train$data)
#-------------Basic Training using XGBoost-----------------
# this is the basic usage of xgboost you can put matrix in data field
# note: we are puting in sparse matrix here, xgboost naturally handles sparse input
# use sparse matrix when your feature is sparse(e.g. when you using one-hot encoding vector)
# note: we are putting in sparse matrix here, xgboost naturally handles sparse input
# use sparse matrix when your feature is sparse(e.g. when you are using one-hot encoding vector)
print("training xgboost with sparseMatrix")
bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2,
nthread = 2, objective = "binary:logistic")
@ -22,7 +22,7 @@ print("training xgboost with Matrix")
bst <- xgboost(data = as.matrix(train$data), label = train$label, max.depth = 2, eta = 1, nround = 2,
nthread = 2, objective = "binary:logistic")
# you can also put in xgb.DMatrix object, stores label, data and other meta datas needed for advanced features
# you can also put in xgb.DMatrix object, which stores label, data and other meta datas needed for advanced features
print("training xgboost with xgb.DMatrix")
dtrain <- xgb.DMatrix(data = train$data, label = train$label)
bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2, nthread = 2,
@ -72,7 +72,7 @@ print(paste("sum(abs(pred3-pred))=", sum(abs(pred2-pred))))
dtrain <- xgb.DMatrix(data = train$data, label=train$label)
dtest <- xgb.DMatrix(data = test$data, label=test$label)
#---------------Using watchlist----------------
# watchlist is a list of xgb.DMatrix, each of them tagged with name
# watchlist is a list of xgb.DMatrix, each of them is tagged with name
watchlist <- list(train=dtrain, test=dtest)
# to train with watchlist, use xgb.train, which contains more advanced features
# watchlist allows us to monitor the evaluation result on all data in the list