Generate in-memory random data without using DMatrix.

! \brief Element from a sparse vector */

SparkXGBClassifier is a PySpark ML estimator. It implements the XGBoost classification algorithm based on XGBoost python library, and it can be us

Data Matrix used in XGBoost. DMatrix is an internal data structure that is used by XGBoost, which is optimized for both memory efficiency and

DMatrix for xgboost. @author hzx

A DMatrix variant that generates quantilized data directly from input for the ``hist`` tree method. This DMatrix is primarily designed to save mem

* Range iterator */

DMatrix holding on references to Dask DataFrame or Dask Array. Constructing a `DaskDMatrix` forces all lazy computation to be carried out. Wait

Iterator for testing streaming DMatrix. (external memory, quantile)

Wrapper type for communicating with numpy and cupy.

SparkXGBRegressor is a PySpark ML estimator. It implements the XGBoost regression algorithm based on XGBoost python library, and it can be used in

Contains a dataset in numpy format as well as the relevant objective and metric.

Tracker for the collective used in XGBoost, acting as a coordinator between workers. Parameters ---------- n_workers: The t

User configuration for the communicator context. This is used for easier integration with distributed frameworks. Users of the collective module c

Protocol for communicating with the local error handler during error or shutdown. Only one protocol that doesn't have the tracker involved.

Booster for xgboost, this is a model API that support interactive build of an XGBoost Model

The external memory version of the :py:class:`QuantileDMatrix`. See :doc:`/tutorials/external_memory` for explanation and usage examples, and

a static class for handling version info

Error thrown by xgboost trainer.

* Adapter for dense array on host, in Python that's `numpy.ndarray`. This is similar to * `DenseAdapter`, but supports __array_interface__ instead o

tree growing policies */

Split grad is the same as value grad.

Callback for testing multi-output.

An iterator for testing categorical features.

A structure containing information about generated user-click data.

Use mean as split grad.

QuantileDMatrix will only be used to train

Java implementation of the Rabit tracker to coordinate distributed workers.

SparkXGBRanker is a PySpark ML estimator. It implements the XGBoost ranking algorithm based on XGBoost python library, and it can be used in P

A Booster of XGBoost. Booster is the model of xgboost, that contains low level routines for training, prediction and evaluation.

"Auxiliary datastruct to hold one fold of CV.

A special internal callback for invoking a list of other callbacks. .. versionadded:: 1.3.0

An internal storage class for categories returned by the DMatrix and the Booster. This class is designed to be opaque. It is intended to be used e

A context controlling collective communicator initialization and finalization.

Callback function for early stopping .. versionadded:: 1.3.0 Parameters ---------- rounds : Early stopping rounds. metri

Print the evaluation result at each iteration. .. versionadded:: 1.3.0 Parameters ---------- rank : Which worker should be

Callback function for scheduling learning rate. .. versionadded:: 1.3.0 Parameters ---------- learning_rates : If it's a ca

Protocol for communicating with the tracker for printing message.

Simple data struct for holding a train-test split of a learning to rank dataset.

A placeholder class when DMatrix cannot be constructed (QuantileDMatrix, inplace_predict).

A storage class for transformed arrow table.

Configurations use when building libxgboost

* @brief Adapter for CSR array on host, in Python that's `scipy.sparse.csr_matrix`. */

clang tidy wrapper. Args: args: Command line arguments. cpp_lint: Run linter on C++ source code. cuda_lint: Run linter

A context controlling collective communicator initialization and finalization.

Context with PySpark specific task ID.

A storage class for transformed cuDF dataframe.

A custom early stopping class where early stopping is determined stochastically. In the beginning, allow the metric to become worse with a probabi

A data iterator for the `DaskQuantileDMatrix`.

A dask version of :py:class:`QuantileDMatrix`. See :py:class:`DaskDMatrix` for parameter documents.

Supported data split mode for DMatrix.

* @brief A page stored in ELLPACK format. * * This class uses the PImpl idiom (https://en.cppreference.com/w/cpp/language/pimpl) to avoid * includi

Note: this class is used as a throwable exception. Some xgboost C functions that use callbacks will catch exceptions that happen inside of the callbac

Tracker for federated training. Parameters ---------- n_workers : The number of federated workers. port : The port t

A data iterator for XGBoost DMatrix. `reset` and `next` are required for any data iterator, other functions here are utilites for demonstrati

A data iterator for XGBoost DMatrix. `reset` and `next` are required for any data iterator, other functions here are utilites for demonstrati

A custom iterator for loading files in batches.

A custom iterator for loading files in batches.

logistic loss for probability regression task

No split grad.