# 開始使用 XGBoost
這里是一個快速入門的教程, 它展示了讓你快速在示例數據集上進行二元分類任務時的 xgboost 的代碼片段.
## Links to Helpful Other Resources
* 請參閱 [_安裝指南_](../build.html) 以了解如何去安裝 xgboost.
* 請參閱 [_入門指引_](../how_to/index.html) 以了解有關使用 xgboost 的各種技巧.
* 請參閱 [_學習教程_](../tutorials/index.html) 以了解有關特定任務的教程.
* 請參閱 [通過例子來學習使用 XGBoost](https://github.com/dmlc/xgboost/tree/master/doc/../demo) 以了解更多代碼示例.
## Python
```
import xgboost as xgb
# 讀取數據
dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')
dtest = xgb.DMatrix('demo/data/agaricus.txt.test')
# 通過 map 指定參數
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
num_round = 2
bst = xgb.train(param, dtrain, num_round)
# 預測
preds = bst.predict(dtest)
```
## R
```
# 加載數據
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
# 擬合模型
bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2,
nthread = 2, objective = "binary:logistic")
# 預測
pred <- predict(bst, test$data)
```
## Julia
```
using XGBoost
# 讀取數據
train_X, train_Y = readlibsvm("demo/data/agaricus.txt.train", (6513, 126))
test_X, test_Y = readlibsvm("demo/data/agaricus.txt.test", (1611, 126))
# 擬合模型
num_round = 2
bst = xgboost(train_X, num_round, label=train_Y, eta=1, max_depth=2)
# 預測
pred = predict(bst, test_X)
```
## Scala
```
import ml.dmlc.xgboost4j.scala.DMatrix
import ml.dmlc.xgboost4j.scala.XGBoost
object XGBoostScalaExample {
def main(args: Array[String]) {
// 讀取 xgboost/demo/data 目錄中可用的訓練數據
val trainData =
new DMatrix("/path/to/agaricus.txt.train")
// 定義參數
val paramMap = List(
"eta" -> 0.1,
"max_depth" -> 2,
"objective" -> "binary:logistic").toMap
// 迭代次數
val round = 2
// train the model
val model = XGBoost.train(trainData, paramMap, round)
// 預測
val predTrain = model.predict(trainData)
// 保存模型至文件
model.saveModel("/local/path/to/model")
}
}
```
