Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

replace nround with nrounds to match actual parameter (#3592)

Browse Source
This commit is contained in:
Jakob Richter
2018-08-15 20:13:53 +02:00
committed by Philip Hyunsu Cho
parent 73bd590a1d
commit 725f4c36f2
17 changed files with 51 additions and 51 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
R-package/demo/cross_validation.R
View File

@@ -5,20 +5,20 @@ data(agaricus.test, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
nround <- 2
nrounds <- 2
param <- list(max_depth=2, eta=1, silent=1, nthread=2, objective='binary:logistic')
cat('running cross validation\n')
# do cross validation, this will print result out as
# [iteration] metric_name:mean_value+std_value
# std_value is standard deviation of the metric
xgb.cv(param, dtrain, nround, nfold=5, metrics={'error'})
xgb.cv(param, dtrain, nrounds, nfold=5, metrics={'error'})
cat('running cross validation, disable standard deviation display\n')
# do cross validation, this will print result out as
# [iteration] metric_name:mean_value+std_value
# std_value is standard deviation of the metric
xgb.cv(param, dtrain, nround, nfold=5,
xgb.cv(param, dtrain, nrounds, nfold=5,
metrics='error', showsd = FALSE)
###
@@ -43,9 +43,9 @@ evalerror <- function(preds, dtrain) {
param <- list(max_depth=2, eta=1, silent=1,
objective = logregobj, eval_metric = evalerror)
# train with customized objective
xgb.cv(params = param, data = dtrain, nrounds = nround, nfold = 5)
xgb.cv(params = param, data = dtrain, nrounds = nrounds, nfold = 5)
# do cross validation with prediction values for each fold
res <- xgb.cv(params = param, data = dtrain, nrounds = nround, nfold = 5, prediction = TRUE)
res <- xgb.cv(params = param, data = dtrain, nrounds = nrounds, nfold = 5, prediction = TRUE)
res$evaluation_log
length(res$pred)

4
R-package/demo/predict_first_ntree.R
View File

@@ -7,10 +7,10 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
watchlist <- list(eval = dtest, train = dtrain)
nround = 2
nrounds = 2
# training the model for two rounds
bst = xgb.train(param, dtrain, nround, nthread = 2, watchlist)
bst = xgb.train(param, dtrain, nrounds, nthread = 2, watchlist)
cat('start testing prediction from first n trees\n')
labels <- getinfo(dtest,'label')

6
R-package/demo/predict_leaf_indices.R
View File

@@ -11,10 +11,10 @@ dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
nround = 4
nrounds = 4
# training the model for two rounds
bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2)
bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
# Model accuracy without new features
accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
@@ -43,7 +43,7 @@ new.features.test <- create.new.tree.features(bst, agaricus.test$data)
new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
watchlist <- list(train = new.dtrain)
bst <- xgb.train(params = param, data = new.dtrain, nrounds = nround, nthread = 2)
bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
# Model accuracy with new features
accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)