class_docstr: linear model
      
class_docstr_tok: ["linear","model"]
code: def __init__(
      self,
      model_type="lr",
      task="binary",
      metric="auc",
      block_size=500,
      lr=0.2,
      k=4,
      reg_lambda=0.1,
      init=0.1,
      fold=1,
      epoch=5,
      stop_window=2,
      opt="sgd",
      nthread=None,
      n_jobs=4,
      alpha=1,
      beta=1,
      lambda_1=1,
      lambda_2=1,
      seed=1,
      **kwargs,
  ):
      assert (
          model_type == "lr"
      ), "Model type must be set to lr for LRModel"
      super(LRModel, self).__init__(
          model_type,
          task,
          metric,
          block_size,
          lr,
          k,
          reg_lambda,
          init,
          fold,
          epoch,
          stop_window,
          opt,
          nthread,
          n_jobs,
          alpha,
          beta,
          lambda_1,
          lambda_2,
          seed,
          **kwargs,
      )
  
imports: import os
  import shutil
  import tempfile
  import warnings
  import numpy
  from .xlearn import create_linear, create_fm, create_ffm
  from .data import DMatrix
  from sklearn.base import BaseEstimator
  from sklearn.utils import check_array, check_X_y
label_desc: Use image node_id 1 to create a new LRModel object from inherited base classes: BaseXLearnModel with example: obj = LRModel(model_type, task, metric, block_size, lr, k, reg_lambda, init, fold, epoch, stop_window, opt, nthread, n_jobs, alpha, beta, lambda_1, lambda_2, seed)