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 _select_bool(self, df, features):
      return dict(bool=features)
  
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 17 for calling the LinearModel obj's underlying member method code with example usage: obj._select_bool(df, features) and returns: dict