class_docstr: Linear model (scikit-learn): https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
  
  Model backend differs depending on problem_type:
  
      'binary' & 'multiclass': https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
  
      'regression': https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge
code: def _set_default_params(self):
      default_params = {"random_state": 0, "fit_intercept": True}
      if self.problem_type != REGRESSION:
          default_params.update(
              {"solver": _get_solver(self.problem_type)}
          )
      default_params.update(get_param_baseline())
      for param, val in default_params.items():
          self._set_default_param_value(param, val)
  
imports: import logging
  import re
  import time
  import warnings
  from collections import defaultdict
  import numpy
  from sklearn.compose import ColumnTransformer
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import QuantileTransformer, StandardScaler
  from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_FLOAT, R_INT, R_OBJECT, S_BOOL, S_TEXT_AS_CATEGORY
  from autogluon.core.constants import BINARY, REGRESSION
  from autogluon.core.models import AbstractModel
  from autogluon.core.utils.exceptions import TimeLimitExceeded
  from .hyperparameters.parameters import IGNORE, INCLUDE, ONLY, _get_solver, get_param_baseline, preprocess_params_set
  from .hyperparameters.searchspaces import get_default_searchspace
  from .lr_preprocessing_utils import NlpDataPreprocessor, OheFeaturesGenerator
label_desc: Use image node_id 8 for calling the LinearModel obj's underlying member method code with example usage: obj._set_default_params() without return types