predict.xgb.Booster

Predict method for eXtreme Gradient Boosting model

Predicted values based on either xgboost model or model handle object.

Usage

"predict"(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE, reshape = FALSE, ...)
"predict"(object, ...)

Details

Note that ntreelimit is not necessarily equal to the number of boosting iterations and it is not necessarily equal to the number of trees in a model. E.g., in a random forest-like model, ntreelimit would limit the number of trees. But for multiclass classification, there are multiple trees per iteration, but ntreelimit limits the number of boosting iterations.

Also note that ntreelimit would currently do nothing for predictions from gblinear, since gblinear doesn't keep its boosting history.

One possible practical applications of the predleaf option is to use the model as a generator of new features which capture non-linearity and interactions, e.g., as implemented in xgb.create.features.

See Also

xgb.train.

Aliases

• predict.xgb.Booster
• predict.xgb.Booster.handle

Examples

## binary classification:

data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test

bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
# use all trees by default
pred <- predict(bst, test$data)
# use only the 1st tree
pred <- predict(bst, test$data, ntreelimit = 1)


## multiclass classification in iris dataset:

lb <- as.numeric(iris$Species) - 1
num_class <- 3
set.seed(11)
bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
               max_depth = 4, eta = 0.5, nthread = 2, nrounds = 10, subsample = 0.5,
               objective = "multi:softprob", num_class = num_class)
# predict for softmax returns num_class probability numbers per case:
pred <- predict(bst, as.matrix(iris[, -5]))
str(pred)
# reshape it to a num_class-columns matrix
pred <- matrix(pred, ncol=num_class, byrow=TRUE)
# convert the probabilities to softmax labels
pred_labels <- max.col(pred) - 1
# the following should result in the same error as seen in the last iteration
sum(pred_labels != lb)/length(lb)

# compare that to the predictions from softmax:
set.seed(11)
bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
               max_depth = 4, eta = 0.5, nthread = 2, nrounds = 10, subsample = 0.5,
               objective = "multi:softmax", num_class = num_class)
pred <- predict(bst, as.matrix(iris[, -5]))
str(pred)
all.equal(pred, pred_labels)
# prediction from using only 5 iterations should result 
# in the same error as seen in iteration 5:
pred5 <- predict(bst, as.matrix(iris[, -5]), ntreelimit=5)
sum(pred5 != lb)/length(lb)


## random forest-like model of 25 trees for binary classification:

set.seed(11)
bst <- xgboost(data = train$data, label = train$label, max_depth = 5,
               nthread = 2, nrounds = 1, objective = "binary:logistic",
               num_parallel_tree = 25, subsample = 0.6, colsample_bytree = 0.1)
# Inspect the prediction error vs number of trees:
lb <- test$label
dtest <- xgb.DMatrix(test$data, label=lb)
err <- sapply(1:25, function(n) {
  pred <- predict(bst, dtest, ntreelimit=n)
  sum((pred > 0.5) != lb)/length(lb)
})
plot(err, type='l', ylim=c(0,0.1), xlab='#trees')