Return the predicted value for each sample. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_fe

! * \brief Get weights for queries, if not exists, will return nullptr * \return Pointer of weights for queries */

! \brief Sparsity of this bin ( num_zero_bins / num_data ) */

! \brief Get feature of specific split*/

! * \brief Mapping bin into feature value * \param bin * \return Feature value of this bin */

implement custom average to boost from (if enabled among options)

! \brief not ordered bin for dense feature */

! \brief Get eval result */

! * \brief Get size of prediction at data_idx data * \param data_idx 0: training data, 1: 1st validation data * \return The size of prediction

! * \brief Init the feature histogram * \param feature the feature data for this histogram * \param min_num_data_one_leaf minimal number of data

! * \brief Move data from one index to another index * \param src_idx * \param dst_idx */