class_docstr: Linear regression.
  
  Currently wrapper around linear models in scikit-learn: https://github.com/scikit-learn/scikit-learn
class_docstr_tok: ["Linear","regression",".","Currently","wrapper","around","linear","models","in","scikit-learn",":","https",":","\/\/github.com\/scikit-learn\/scikit-learn"]
docstr: Fits model to provided instances.
  
  Args:
      X: Numpy array for training instances.
      y: Numpy array as training labels.
  
  Returns:
      Itself.
docstr_tok: ["Fits","model","to","provided","instances",".","Args",":","X",":","Numpy","array","for","training","instances",".","y",":","Numpy","array","as","training","labels",".","Returns",":","Itself","."]
code: def fit(self, X, y):
      """Fits model to provided instances.
  
      Args:
          X: Numpy array for training instances.
          y: Numpy array as training labels.
  
      Returns:
          Itself.
      """
      self.sk_model_ = self.linear_class(**self.kwargs)
      return super().fit(X, y)
  
imports: from ..api.base import ExplainerMixin
  from ..api.templates import FeatureValueExplanation
  from ..utils._explanation import gen_name_from_class, gen_global_selector, gen_local_selector
  from ..utils._explanation import gen_perf_dicts
  from abc import abstractmethod
  from sklearn.base import is_classifier
  from sklearn.utils.validation import check_is_fitted
  import numpy
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.linear_model import LogisticRegression
  from sklearn.linear_model import LinearRegression
  from ..utils._clean_x import preclean_X
  from ..utils._clean_simple import clean_dimensions, typify_classification
  from ..utils._unify_data import unify_data
label_desc: Use image node_id 3 for calling the LinearRegression obj's underlying member method code with example usage: obj.fit(X, y) and returns: super