docstr: Learn model from training data.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Training vectors, where n_samples is the number of samples and
      n_features is the number of features.
  y : array-like, shape = [n_samples]
      Target values.
  init_params : bool (default: True)
      Re-initializes model parameters prior to fitting.
      Set False to continue training with weights from
      a previous model fitting.
  
  Returns
  -------
  self : object
code: def fit(self, X, y, init_params=True):
      """Learn model from training data.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape = [n_samples, n_features]
          Training vectors, where n_samples is the number of samples and
          n_features is the number of features.
      y : array-like, shape = [n_samples]
          Target values.
      init_params : bool (default: True)
          Re-initializes model parameters prior to fitting.
          Set False to continue training with weights from
          a previous model fitting.
  
      Returns
      -------
      self : object
  
      """
      self._is_fitted = False
      self._check_arrays(X=X, y=y)
      self._check_target_array(y)
      if hasattr(self, "self.random_seed") and self.random_seed:
          self._rgen = np.random.RandomState(self.random_seed)
      self._init_time = time()
      self._fit(X=X, y=y, init_params=init_params)
      self._is_fitted = True
      return self
  
file_path: mlxtend/mlxtend/_base/_classifier.py
imports: from time import time
  import numpy
label_desc: Use image node_id 4 for calling the _Classifier obj's underlying member method code with example usage: obj.fit(X, y, init_params) and returns: self