docstr: Predict targets from X.
  
  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.
  
  Returns
  ----------
  target_values : array-like, shape = [n_samples]
    Predicted target values.
code: def predict(self, X):
      """Predict targets from X.
  
      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.
  
      Returns
      ----------
      target_values : array-like, shape = [n_samples]
        Predicted target values.
  
      """
      self._check_arrays(X=X)
      if not self._is_fitted:
          raise AttributeError("Model is not fitted, yet.")
      return self._predict(X)
  
file_path: mlxtend/mlxtend/_base/_classifier.py
imports: from time import time
  import numpy
label_desc: Use image node_id 5 for calling the _Classifier obj's underlying member method code with example usage: obj.predict(X) and returns: self