docstr: Checks if the given feature is numerical.
  
  Parameters
  ----------
  feature
      Feature to be checked.
  
  Returns
  -------
  ``True`` if the feature is numerical. ``False`` otherwise.
docstr_tok: ["Checks","if","the","given","feature","is","numerical",".","Parameters","--","--","--","--","--","feature","Feature","to","be","checked",".","Returns","--","--","--","-","``","True","``","if","the","feature","is","numerical",".","``","False","``","otherwise","."]
code: def _is_numerical(self, feature):
      """
      Checks if the given feature is numerical.
  
      Parameters
      ----------
      feature
          Feature to be checked.
  
      Returns
      -------
      ``True`` if the feature is numerical. ``False`` otherwise.
      """
      return feature not in self.categorical_names
  
imports: import copy
  import logging
  import math
  import numbers
  import sys
  from abc import ABC, abstractmethod
  from enum import Enum
  from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union, no_type_check
  import matplotlib.pyplot
  import numpy
  from scipy.stats.mstats import mquantiles
  from sklearn.base import BaseEstimator, is_classifier, is_regressor
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.ensemble._gb import BaseGradientBoosting
  from sklearn.ensemble._hist_gradient_boosting.gradient_boosting import BaseHistGradientBoosting
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.utils.extmath import cartesian
  from sklearn.utils.validation import check_is_fitted
  from tqdm import tqdm
  from alibi.api.defaults import DEFAULT_DATA_PD, DEFAULT_META_PD
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.explainers.ale import get_quantiles
  from alibi.utils import _get_options_string
label_desc: Use image node_id 8 for calling the PartialDependenceBase obj's underlying member method code with example usage: obj._is_numerical(feature) and returns: unknown