Python Package Introduction

This document gives a basic walkthrough of the xgboost package for Python. The Python package is consisted of 3 different interfaces, including native interface, scikit-learn interface and dask interface. For introduction to dask interface please see Distributed XGBoost with Dask.

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Install XGBoost

To install XGBoost, follow instructions in Installation Guide.

To verify your installation, run the following in Python:

import xgboost as xgb

Data Interface

The XGBoost python module is able to load data from many different types of data format, including:

  • NumPy 2D array

  • SciPy 2D sparse array

  • Pandas data frame

  • cuDF DataFrame

  • cupy 2D array

  • dlpack

  • datatable

  • XGBoost binary buffer file.

  • LIBSVM text format file

  • Comma-separated values (CSV) file

  • Arrow table.

(See Text Input Format of DMatrix for detailed description of text input format.)

The data is stored in a DMatrix object.

  • To load a NumPy array into DMatrix:

    data = np.random.rand(5, 10)  # 5 entities, each contains 10 features
    label = np.random.randint(2, size=5)  # binary target
    dtrain = xgb.DMatrix(data, label=label)
  • To load a scipy.sparse array into DMatrix:

    csr = scipy.sparse.csr_matrix((dat, (row, col)))
    dtrain = xgb.DMatrix(csr)
  • To load a Pandas data frame into DMatrix:

    data = pandas.DataFrame(np.arange(12).reshape((4,3)), columns=['a', 'b', 'c'])
    label = pandas.DataFrame(np.random.randint(2, size=4))
    dtrain = xgb.DMatrix(data, label=label)
  • Saving DMatrix into a XGBoost binary file will make loading faster:

    dtrain = xgb.DMatrix('train.svm.txt')
  • Missing values can be replaced by a default value in the DMatrix constructor:

    dtrain = xgb.DMatrix(data, label=label, missing=np.NaN)
  • Weights can be set when needed:

    w = np.random.rand(5, 1)
    dtrain = xgb.DMatrix(data, label=label, missing=np.NaN, weight=w)

When performing ranking tasks, the number of weights should be equal to number of groups.

  • To load a LIBSVM text file or a XGBoost binary file into DMatrix:

    dtrain = xgb.DMatrix('train.svm.txt')
    dtest = xgb.DMatrix('test.svm.buffer')

    The parser in XGBoost has limited functionality. When using Python interface, it’s recommended to use sklearn load_svmlight_file or other similar utilites than XGBoost’s builtin parser.

  • To load a CSV file into DMatrix:

    # label_column specifies the index of the column containing the true label
    dtrain = xgb.DMatrix('train.csv?format=csv&label_column=0')
    dtest = xgb.DMatrix('test.csv?format=csv&label_column=0')

    The parser in XGBoost has limited functionality. When using Python interface, it’s recommended to use pandas read_csv or other similar utilites than XGBoost’s builtin parser.

Setting Parameters

XGBoost can use either a list of pairs or a dictionary to set parameters. For instance:

  • Booster parameters

    param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
    param['nthread'] = 4
    param['eval_metric'] = 'auc'
  • You can also specify multiple eval metrics:

    param['eval_metric'] = ['auc', 'ams@0']
    # alternatively:
    # plst = param.items()
    # plst += [('eval_metric', 'ams@0')]
  • Specify validations set to watch performance

    evallist = [(dtrain, 'train'), (dtest, 'eval')]


Training a model requires a parameter list and data set.

num_round = 10
bst = xgb.train(param, dtrain, num_round, evallist)

After training, the model can be saved.


The model and its feature map can also be dumped to a text file.

# dump model
# dump model with feature map
bst.dump_model('dump.raw.txt', 'featmap.txt')

A saved model can be loaded as follows:

bst = xgb.Booster({'nthread': 4})  # init model
bst.load_model('model.bin')  # load data

Methods including update and boost from xgboost.Booster are designed for internal usage only. The wrapper function xgboost.train does some pre-configuration including setting up caches and some other parameters.

Early Stopping

If you have a validation set, you can use early stopping to find the optimal number of boosting rounds. Early stopping requires at least one set in evals. If there’s more than one, it will use the last.

train(..., evals=evals, early_stopping_rounds=10)

The model will train until the validation score stops improving. Validation error needs to decrease at least every early_stopping_rounds to continue training.

If early stopping occurs, the model will have two additional fields: bst.best_score, bst.best_iteration. Note that xgboost.train() will return a model from the last iteration, not the best one.

This works with both metrics to minimize (RMSE, log loss, etc.) and to maximize (MAP, NDCG, AUC). Note that if you specify more than one evaluation metric the last one in param['eval_metric'] is used for early stopping.


A model that has been trained or loaded can perform predictions on data sets.

# 7 entities, each contains 10 features
data = np.random.rand(7, 10)
dtest = xgb.DMatrix(data)
ypred = bst.predict(dtest)

If early stopping is enabled during training, you can get predictions from the best iteration with bst.best_iteration:

ypred = bst.predict(dtest, iteration_range=(0, bst.best_iteration + 1))


You can use plotting module to plot importance and output tree.

To plot importance, use xgboost.plot_importance(). This function requires matplotlib to be installed.


To plot the output tree via matplotlib, use xgboost.plot_tree(), specifying the ordinal number of the target tree. This function requires graphviz and matplotlib.

xgb.plot_tree(bst, num_trees=2)

When you use IPython, you can use the xgboost.to_graphviz() function, which converts the target tree to a graphviz instance. The graphviz instance is automatically rendered in IPython.

xgb.to_graphviz(bst, num_trees=2)

Scikit-Learn interface

XGBoost provides an easy to use scikit-learn interface for some pre-defined models including regression, classification and ranking.

# Use "gpu_hist" for training the model.
reg = xgb.XGBRegressor(tree_method="gpu_hist")
# Fit the model using predictor X and response y., y)
# Save model into JSON format.

User can still access the underlying booster model when needed:

booster: xgb.Booster = reg.get_booster()