Indicates a number of rows/columns must be inferred

evaluate with predicts and data @param predicts predictions as array @param dmat data matrix to evaluate @return result of the metric

Gets a logger by name, or creates and configures it for the first time.

Get feature importance of each feature. For tree model Importance type can be defined as: * 'weight': the number of times a feature i

Get xgboost specific parameters.

Assert whether two DMatrices contain the same predictors.

Run a snippet of code.

Slice the DMatrix and return a new DMatrix that only contains `rindex`. Parameters ---------- rindex List of indi

Constructed as [1, n_samples] if group ptr is not supplied by the user

! * \brief Split a string by delimiter * \param s String to be split. * \param delim The delimiter. */

Whether the input is a dataframe. Currently supported dataframes: - pandas - cudf - cudf.pandas - polars - pyarrow - modin

NOLINT

Train a xgboost model with link. @param dtrain The training data. @param params XGBoost parameters. @param numBoostRound Number of rounds to train.

Generate prediction value ranging from 2**1 to 2**15, using grid points in log scale Then check prediction against the reference values

* \brief Whether should it traverse to left branch of a tree. * * Go to left if it's NOT the matching category, which matches one-hot encoding. *

If gpu is used to do the prediction according to the parameters and spark configurations

Close the R session.

Concatenate row-wise.

Create a rank list by model prediction

!\return feature index to split on */

* \brief Obtain a reference to the raw data. */

xgboost implementation

Check if stage-level scheduling is not needed, return true to skip stage-level scheduling

get label values @return label @throws XGBoostError native error

Get float property from the DMatrix. Parameters ---------- field: str The field name of the information.

Wrapper for testing pandas category type.

Save DMatrix to an XGBoost buffer. Saved binary can be later loaded by providing the path to :py:func:`xgboost.DMatrix` as input. Pa

Set base margin of booster to start from. This can be used to specify a prediction value of existing model to be base_margin However,

Set group size of DMatrix (used for ranking). Parameters ---------- group : array like Group size of each group

30min