predict.xgb.Booster {xgboost} | R Documentation |
Predicted values based on either xgboost model or model handle object.
## S3 method for class 'xgb.Booster' predict(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE, reshape = FALSE, ...) ## S3 method for class 'xgb.Booster.handle' predict(object, ...)
object |
Object of class |
newdata |
takes |
missing |
Missing is only used when input is dense matrix. Pick a float value that represents missing values in data (e.g., sometimes 0 or some other extreme value is used). |
outputmargin |
whether the prediction should be returned in the for of original untransformed
sum of predictions from boosting iterations' results. E.g., setting |
ntreelimit |
limit the number of model's trees or boosting iterations used in prediction (see Details).
It will use all the trees by default ( |
predleaf |
whether predict leaf index instead. |
reshape |
whether to reshape the vector of predictions to a matrix form when there are several
prediction outputs per case. This option has no effect when |
... |
Parameters passed to |
Note that ntreelimit
is not necesserily equal to the number of boosting iterations
and it is not necesserily 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
.
For regression or binary classification, it returns a vector of length nrows(newdata)
.
For multiclass classification, either a num_class * nrows(newdata)
vector or
a (nrows(newdata), num_class)
dimension matrix is returned, depending on
the reshape
value.
When predleaf = TRUE
, the output is a matrix object with the
number of columns corresponding to the number of trees.
## 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')