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doc/get_started/index.md

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+# Get Started with XGBoost
+
+This is a quick started tutorial showing snippets for you to quickly try out xgboost
+on the demo dataset on a binary classification task.
+
+## Links to Helpful Other Resources
+- See [Installation Guide](../build.md) on how to install xgboost.
+- See [How to pages](../how_to/index.md) on various tips on using xgboost.
+- See [Tutorials](../tutorials/index.md) on tutorials on specific tasks.
+- See [Learning to use XGBoost by Examples](../../demo) for more code examples.
+
+## Python
+```python
+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)
+```
+
+## R
+
+```r
+# 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)
+
+```
+
+## Julia
+```julia
+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)
+```
+
+## Scala
+```scala
+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")
+  }
+}
+```