######################## Get Started with XGBoost ########################

This is a quick start tutorial showing snippets for you to quickly try out XGBoost on the demo dataset on a binary classification task.

---

Links to Other Helpful Resources

---

• See :doc:Installation Guide </install> on how to install XGBoost.
• See :doc:Text Input Format </tutorials/input_format> on using text format for specifying training/testing data.
• See :doc:Tutorials </tutorials/index> for tips and tutorials.
• See Learning to use XGBoost by Examples <https://github.com/dmlc/xgboost/tree/master/demo>_ for more code examples.

---

Python

---

.. code-block:: python

from xgboost import XGBClassifier

read data

from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split data = load_iris() X_train, X_test, y_train, y_test = train_test_split(data['data'], data['target'], test_size=.2)

create model instance

bst = XGBClassifier(n_estimators=2, max_depth=2, learning_rate=1, objective='binary:logistic')

fit model

bst.fit(X_train, y_train)

make predictions

preds = bst.predict(X_test)

---

R

---

.. code-block:: R

load data

data(agaricus.train, package='xgboost') data(agaricus.test, package='xgboost') train <- agaricus.train test <- agaricus.test

fit model

bst <- xgboost(x = train$data, y = factor(train$label), max.depth = 2, eta = 1, nrounds = 2, nthread = 2, objective = "binary:logistic")

predict

pred <- predict(bst, test$data)

---

Julia

---

.. code-block:: 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

---

.. code-block:: 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") } }