class_docstr: Fits and predicts the target on the test dataset.
  
  The test dataset must not contain the target values.
  
  Parameters
  ----------
  to_path : str, default = "save"
      Name of the folder where feature importances and
      predictions are saved (.png and .csv formats).
      Must contain target encoder object (for classification task only).
  
  verbose : bool, default = True
      Verbose mode
code: def set_params(self, **params):
      self.__fitOK = False
  
      for k, v in params.items():
          if k not in self.get_params():
              warnings.warn(
                  "Invalid parameter a for predictor Predictor. "
                  "Parameter IGNORED. "
                  "Check the list of available parameters with "
                  "`predictor.get_params().keys()`"
              )
          else:
              setattr(self, k, v)
  
imports: import pandas
  import numpy
  import os
  import pickle
  import warnings
  import time
  import operator
  import matplotlib
  import matplotlib.pyplot
  from sklearn.pipeline import Pipeline
  from ..encoding.na_encoder import NA_encoder
  from ..encoding.categorical_encoder import Categorical_encoder
  from ..model.classification.feature_selector import Clf_feature_selector
  from ..model.regression.feature_selector import Reg_feature_selector
  from ..model.classification.stacking_classifier import StackingClassifier
  from ..model.regression.stacking_regressor import StackingRegressor
  from ..model.classification.classifier import Classifier
  from ..model.regression.regressor import Regressor
label_desc: Use image node_id 3 for calling the Predictor obj's underlying member method code with example usage: obj.set_params() without return types