From 3dd40b9f37645b162b0388178928f311e3af6b67 Mon Sep 17 00:00:00 2001 From: Yuan Tang Date: Mon, 10 Aug 2015 20:35:10 -0400 Subject: [PATCH] fixed typos in basic_walkthrough demo --- R-package/demo/basic_walkthrough.R | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/R-package/demo/basic_walkthrough.R b/R-package/demo/basic_walkthrough.R index 762a1c8e8..532c5d873 100644 --- a/R-package/demo/basic_walkthrough.R +++ b/R-package/demo/basic_walkthrough.R @@ -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