class_docstr: Base class for Linear Models
class_docstr_tok: ["Base","class","for","Linear","Models"]
docstr: Predict using the linear model.
  
  Parameters
  ----------
  X : array-like or sparse matrix, shape (n_samples, n_features)
      Samples.
  
  Returns
  -------
  C : array, shape (n_samples,)
      Returns predicted values.
docstr_tok: ["Predict","using","the","linear","model",".","Parameters","--","--","--","--","--","X",":","array-like","or","sparse","matrix",",","shape","(","n_samples",",","n_features",")","Samples",".","Returns","--","--","--","-","C",":","array",",","shape","(","n_samples",",",")","Returns","predicted","values","."]
code: def predict(self, X):
      """
      Predict using the linear model.
  
      Parameters
      ----------
      X : array-like or sparse matrix, shape (n_samples, n_features)
          Samples.
  
      Returns
      -------
      C : array, shape (n_samples,)
          Returns predicted values.
      """
      return self._decision_function(X)
  
imports: import numbers
  from abc import ABCMeta, abstractmethod
  from numpy.linalg import LinAlgError
  from sklearn.utils.validation import check_is_fitted, _deprecate_positional_args
  from sklearn.base import MultiOutputMixin
  from ...None import execute
  from ...None import tensor
  from ...tensor.datasource import tensor
  from ..base import BaseEstimator, RegressorMixin, ClassifierMixin
  from ..preprocessing import normalize
  from ..utils.validation import _check_sample_weight, check_array, FLOAT_DTYPES
label_desc: Use image node_id 3 for calling the LinearModel obj's underlying member method code with example usage: obj.predict(X) and returns: self