class_docstr: Base linear model.
  
  Currently wrapper around linear models in scikit-learn.
  
  https://github.com/scikit-learn/scikit-learn
class_docstr_tok: ["Base","linear","model",".","Currently","wrapper","around","linear","models","in","scikit-learn",".","https",":","\/\/github.com\/scikit-learn\/scikit-learn"]
docstr: Predicts on provided instances.
  
  Args:
      X: Numpy array for instances.
  
  Returns:
      Predicted class label per instance.
docstr_tok: ["Predicts","on","provided","instances",".","Args",":","X",":","Numpy","array","for","instances",".","Returns",":","Predicted","class","label","per","instance","."]
code: def predict(self, X):
      """Predicts on provided instances.
  
      Args:
          X: Numpy array for instances.
  
      Returns:
          Predicted class label per instance.
      """
  
      check_is_fitted(self, "has_fitted_")
  
      X, n_samples = preclean_X(
          X, self.feature_names_in_, self.feature_types_in_
      )
      X, _, _ = unify_data(
          X,
          n_samples,
          self.feature_names_in_,
          self.feature_types_in_,
          False,
          0,
      )
  
      return self._model().predict(X)
  
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 4 for calling the BaseLinear obj's underlying member method code with example usage: obj.predict(X) and returns: self