! * \brief Get number of weak sub-models * \return Number of weak sub-models */

If the custom "average" is implemented it will be used inplace of the label average (if enabled) * * An improvement to this is to have options to expl

! * \brief predicting on data, then saving result to disk * \param data_filename Filename of data * \param result_filename Filename of output re

! * \brief Reset mapper */

! * \brief SetLabelAt used data indices before training, used for bagging * \param used_data_indices indices of used data * \param num_used_data

! * \brief Get sizes in byte of this object */

Boost the booster for one iteration, with customized gradient statistics. Note: for multi-class task, the score is group by class_id

Initialize data from a csc matrix.

Initialize data from a 2-D numpy matrix.

Predict for a csc data

Aggregate cross-validation results.

Decorate an eval function Note: for multi-class task, the y_pred is group by class_id first, then group by row_id. If you want to get i-

Load LightGBM Library.

Make an n-fold list of Booster from random indices.

Decorate an objective function Note: for multi-class task, the y_pred is group by class_id first, then group by row_id. If you want to g

Convert specified tree to graphviz instance. See: - http://graphviz.readthedocs.io/en/stable/api.html#digraph

recursively add node or edge

Return the predicted leaf every tree for each sample. Parameters ---------- X : array-like or sparse matrix of shape = [n_sam

Get a dictionary representation of the model properties. Returns ------- [type] [description]

Get attribute string from the Booster. Parameters ---------- key : string The name of the attribute. Ret

! * \brief Get bin info */

Convert a python array to c array.

Convert a ctypes float pointer array to a numpy array.

Convert a ctypes double pointer array to a numpy array.

Convert a ctypes float pointer array to a numpy array.

Create a digraph representation of specified tree. Note ---- For more information please visit http://graphviz.readthedocs.io/en/stab

Create validation data align with current Dataset. Parameters ---------- data : string, numpy array or scipy.sparse

Comparisons

Estimate propensity for clicks in a list. Parameters ---------- click_list : [type] [description] use_non

fail(msg, err_ret = Json()) * * Mark this parse as failed. */

Find the path to LightGBM library files. Returns ------- lib_path: list(string) List of all found library path to LightGBM

Free network.

not using visual studio in windows

! \brief True if bin is trival (contains only one bin) */

Load a ClickModel child type from a JSON file. Parameters ---------- fpath : str Path to the click model file. Returns -

! * \brief Get ranks, if not exists, will return nullptr * \return Pointer of ranks */