class_docstr: XGBoost-based model, intended for usage in forecasting tasks.
class_docstr_tok: ["XGBoost-based","model",",","intended","for","usage","in","forecasting","tasks","."]
code: def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None:
      log.info("Started fitting LGBM models for array prediction")
      self._fit(train_data, dev_data, submodel_method="fit")
  
file_path: lightwood/lightwood/mixer/xgboost_array.py
imports: from typing import Dict, List, Union, Optional
  from copy import deepcopy
  import numpy
  import pandas
  from lightwood.helpers.log import log
  from lightwood.encoder.base import BaseEncoder
  from lightwood.mixer.base import BaseMixer
  from lightwood.mixer.xgboost import XGBoostMixer
  from lightwood.api.types import PredictionArguments, TimeseriesSettings
  from lightwood.data.encoded_ds import EncodedDs, ConcatedEncodedDs
label_desc: Use image node_id 3 for calling the XGBoostArrayMixer obj's underlying member method code with example usage: obj.fit(train_data, dev_data) without return types