本文是xgboost编程的简单入门。文中给出了一些示例代码片段：使用xgboost解决demo数据集上的二分类问题。
xgboost的Python代码示例
import xgboost as xgb
# read in data
dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')
dtest = xgb.DMatrix('demo/data/agaricus.txt.test')
# specify parameters via map
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
num_round = 2
bst = xgb.train(param, dtrain, num_round)
# make prediction
preds = bst.predict(dtest)
xgboost的R语言示例
# load data
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
# fit model
bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2,
nthread = 2, objective = "binary:logistic")
# predict
pred <- predict(bst, test$data)
xgboost的julia语言例子
using XGBoost
# read data
train_X, train_Y = readlibsvm("demo/data/agaricus.txt.train", (6513, 126))
test_X, test_Y = readlibsvm("demo/data/agaricus.txt.test", (1611, 126))
# fit model
num_round = 2
bst = xgboost(train_X, num_round, label=train_Y, eta=1, max_depth=2)
# predict
pred = predict(bst, test_X)
xgboost的scala例子
import ml.dmlc.xgboost4j.scala.DMatrix
import ml.dmlc.xgboost4j.scala.XGBoost
object XGBoostScalaExample {
def main(args: Array[String]) {
// read trainining data, available at xgboost/demo/data
val trainData =
new DMatrix("/path/to/agaricus.txt.train")
// define parameters
val paramMap = List(
"eta" -> 0.1,
"max_depth" -> 2,
"objective" -> "binary:logistic").toMap
// number of iterations
val round = 2
// train the model
val model = XGBoost.train(trainData, paramMap, round)
// run prediction
val predTrain = model.predict(trainData)
// save model to the file.
model.saveModel("/local/path/to/model")
}
}