## binary classification:
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
## Keep the number of threads to 2 for examples
nthread <- 2
data.table::setDTthreads(nthread)
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
eta = 0.5, nthread = nthread, nrounds = 5, objective = "binary:logistic")
# use all trees by default
pred <- predict(bst, test$data)
# use only the 1st tree
pred1 <- predict(bst, test$data, iterationrange = c(1, 2))
# Predicting tree leafs:
# the result is an nsamples X ntrees matrix
pred_leaf <- predict(bst, test$data, predleaf = TRUE)
str(pred_leaf)
# Predicting feature contributions to predictions:
# the result is an nsamples X (nfeatures + 1) matrix
pred_contr <- predict(bst, test$data, predcontrib = TRUE)
str(pred_contr)
# verify that contributions' sums are equal to log-odds of predictions (up to float precision):
summary(rowSums(pred_contr) - qlogis(pred))
# for the 1st record, let's inspect its features that had non-zero contribution to prediction:
contr1 <- pred_contr[1,]
contr1 <- contr1[-length(contr1)] # drop BIAS
contr1 <- contr1[contr1 != 0] # drop non-contributing features
contr1 <- contr1[order(abs(contr1))] # order by contribution magnitude
old_mar <- par("mar")
par(mar = old_mar + c(0,7,0,0))
barplot(contr1, horiz = TRUE, las = 2, xlab = "contribution to prediction in log-odds")
par(mar = old_mar)
## 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]), iterationrange=c(1, 6))
sum(pred5 != lb)/length(lb)
Run the code above in your browser using DataLab