class_docstr: Fresh Pipeline with RotatIoN forest Classifier.
  
  This classifier simply transforms the input data using the TSFresh [1]_
  transformer with comprehensive features and builds a RotationForest estimator using
  the transformed data.
  
  Parameters
  ----------
  default_fc_parameters : str, default="comprehensive"
      Set of TSFresh features to be extracted, options are "minimal", "efficient" or
      "comprehensive".
  n_estimators : int, default=200
      Number of estimators for the RotationForest ensemble.
  verbose : int, default=0
      Level of output printed to the console (for information only)
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  chunksize : int or None, default=None
      Number of series processed in each parallel TSFresh job, should be optimised
      for efficient parallelisation.
  random_state : int or None, default=None
      Seed for random, integer.
  
  Attributes
  ----------
  n_classes_ : int
      Number of classes. Extracted from the data.
  classes_ : ndarray of shape (n_classes_)
      Holds the label for each class.
  
  See Also
  --------
  TSFreshFeatureExtractor, TSFreshClassifier, RotationForest
  
  References
  ----------
  .. [1] Christ, Maximilian, et al. "Time series feature extraction on basis of
      scalable hypothesis tests (tsfresh–a python package)." Neurocomputing 307
      (2018): 72-77.
      https://www.sciencedirect.com/science/article/pii/S0925231218304843
class_docstr_tok: ["Fresh","Pipeline","with","RotatIoN","forest","Classifier",".","This","classifier","simply","transforms","the","input","data","using","the","TSFresh","[","1","]","_","transformer","with","comprehensive","features","and","builds","a","RotationForest","estimator","using","the","transformed","data",".","Parameters","--","--","--","--","--","default_fc_parameters",":","str",",","default=","''","comprehensive","''","Set","of","TSFresh","features","to","be","extracted",",","options","are","``","minimal","''",",","``","efficient","''","or","``","comprehensive","''",".","n_estimators",":","int",",","default=200","Number","of","estimators","for","the","RotationForest","ensemble",".","verbose",":","int",",","default=0","Level","of","output","printed","to","the","console","(","for","information","only",")","n_jobs",":","int",",","default=1","The","number","of","jobs","to","run","in","parallel","for","both","`","fit","`","and","`","predict","`",".","``","-1","``","means","using","all","processors",".","chunksize",":","int","or","None",",","default=None","Number","of","series","processed","in","each","parallel","TSFresh","job",",","should","be","optimised","for","efficient","parallelisation",".","random_state",":","int","or","None",",","default=None","Seed","for","random",",","integer",".","Attributes","--","--","--","--","--","n_classes_",":","int","Number","of","classes",".","Extracted","from","the","data",".","classes_",":","ndarray","of","shape","(","n_classes_",")","Holds","the","label","for","each","class",".","See","Also","--","--","--","--","TSFreshFeatureExtractor",",","TSFreshClassifier",",","RotationForest","References","--","--","--","--","--","..","[","1","]","Christ",",","Maximilian",",","et","al",".","``","Time","series","feature","extraction","on","basis","of","scalable","hypothesis","tests","(","tsfresh\u2013a","python","package",")",".","''","Neurocomputing","307","(","2018",")",":","72-77.","https",":","\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231218304843"]
docstr: Predict class values of n instances in X.
  
  Parameters
  ----------
  X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
      The data to make predictions for.
  
  Returns
  -------
  y : array-like, shape = [n_instances]
      Predicted class labels.
docstr_tok: ["Predict","class","values","of","n","instances","in","X.","Parameters","--","--","--","--","--","X",":","3D","np.array","of","shape","=","[","n_instances",",","n_dimensions",",","series_length","]","The","data","to","make","predictions","for",".","Returns","--","--","--","-","y",":","array-like",",","shape","=","[","n_instances","]","Predicted","class","labels","."]
code: def _predict(self, X) -> np.ndarray:
      """Predict class values of n instances in X.
  
      Parameters
      ----------
      X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
          The data to make predictions for.
  
      Returns
      -------
      y : array-like, shape = [n_instances]
          Predicted class labels.
      """
      return self._rotf.predict(self._tsfresh.transform(X))
  
file_path: sktime/sktime/classification/feature_based/_fresh_prince.py
imports: import numpy
  from sktime.classification.base import BaseClassifier
  from sktime.classification.sklearn import RotationForest
  from sktime.transformations.panel.tsfresh import TSFreshFeatureExtractor
  from sktime.utils.validation.panel import check_X_y
label_desc: Use image node_id 3 for calling the FreshPRINCE obj's underlying member method code with example usage: obj._predict(X) and returns: self