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########################
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")
}
}
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