Return the reader for loading the model.

Set label of DMatrix from array interface @param column the XGBoost Column to provide the array interface of label column @throws XGBoo

* @brief Samples a feature set. * * @param ctx The runtime context. * @param depth The tree depth of the node at which to sample. * @retu

Get parameters.

Use string representation to identify a type.

Make JSON-based arguments for C functions.

Predict the probability of each `X` example being of a given class. If the model is trained with early stopping, then :py:attr:`best_iteration

\brief Whether is fidx a categorical feature. * * \param ft Feature type for all features. * \param fidx Feature index. * \return Whether featur

! * \brief push back feature map. * \param fid The feature index. * \param fname The feature name. * \param ftype The feature type. */

* @brief The total number of histogram bins (excluding min values.) */

Get unsigned integer property from the DMatrix. Parameters ---------- field: str The field name of the informatio

Import cupy.

Set float type property into the DMatrix. Parameters ---------- field: str The field name of the information

NOLINT

Make array interface str.

* \brief Make learner model param */

! * \brief set the leaf value of the node * \param value leaf value * \param right right index, could be used to store * ad

Get the weight of the DMatrix.

* @brief Return number of targets for input matrix. Right now XGBoost supports only * multi-target regression. */

Make batches of dense data.

Get arguments for workers.

* \brief number of extra nodes besides the root */

* \brief Get number of pairs for each sample */

Ensure the np array has correct type and is contiguous.

Get the base margin of the DMatrix.

Return the `xgboost.core.Booster` instance.

Get the predictors from DMatrix as a CSR matrix. This getter is mostly for testing purposes. If this is a quantized DMatrix then quantized val

Get quantile cuts for quantization. .. versionadded:: 2.0.0

get the row number of DMatrix @return number of rows @throws XGBoostError native error

Evaluate score for data using the last evaluation metric. If the model is trained with early stopping, then :py:attr:`best_iteration` is used

Start the tracker. Once started, the client still need to call the :py:meth:`wait_for` method in order to wait for it to finish (think of it a

Save model into raw byte array in the UBJSON ("ubj") format. @return the saved byte array @throws XGBoostError native error

Configuration before data is known.