class_docstr: Time Series Feature Extraction based on Scalable Hypothesis Tests classifier.
  
  This classifier simply transforms the input data using the TSFresh [1]
  transformer and builds a provided estimator using the transformed data.
  
  Parameters
  ----------
  default_fc_parameters : str, default="efficient"
      Set of TSFresh features to be extracted, options are "minimal", "efficient" or
      "comprehensive".
  relevant_feature_extractor : bool, default=False
      Remove irrelevant features using the FRESH algorithm.
  estimator : sklearn classifier, default=None
      An sklearn estimator to be built using the transformed data. Defaults to a
      Random Forest with 200 trees.
  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, TSFreshRelevantFeatureExtractor
  
  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
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docstr: Predict class probabilities for n instances in X.
  
  Parameters
  ----------
  X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
      The data to make predict probabilities for.
  
  Returns
  -------
  y : array-like, shape = [n_instances, n_classes_]
      Predicted probabilities using the ordering in classes_.
docstr_tok: ["Predict","class","probabilities","for","n","instances","in","X.","Parameters","--","--","--","--","--","X",":","3D","np.array","of","shape","=","[","n_instances",",","n_dimensions",",","series_length","]","The","data","to","make","predict","probabilities","for",".","Returns","--","--","--","-","y",":","array-like",",","shape","=","[","n_instances",",","n_classes_","]","Predicted","probabilities","using","the","ordering","in","classes_","."]
code: def _predict_proba(self, X) -> np.ndarray:
      """Predict class probabilities for n instances in X.
  
      Parameters
      ----------
      X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
          The data to make predict probabilities for.
  
      Returns
      -------
      y : array-like, shape = [n_instances, n_classes_]
          Predicted probabilities using the ordering in classes_.
      """
      if self._return_majority_class:
          dists = np.zeros((X.shape[0], self.n_classes_))
          dists[:, self._majority_class] = 1
          return dists
  
      m = getattr(self._estimator, "predict_proba", None)
      if callable(m):
          return self._estimator.predict_proba(
              self._transformer.transform(X)
          )
      else:
          dists = np.zeros((X.shape[0], self.n_classes_))
          X_t = self._transformer.transform(X)
          X_t = X_t.reindex(self._Xt_colnames, axis=1, fill_value=0)
          preds = self._estimator.predict(X_t)
          for i in range(0, X.shape[0]):
              dists[i, self._class_dictionary[preds[i]]] = 1
          return dists
  
file_path: sktime/sktime/classification/feature_based/_tsfresh_classifier.py
imports: import numpy
  from sklearn.ensemble import RandomForestClassifier
  from sktime.base._base import _clone_estimator
  from sktime.classification.base import BaseClassifier
  from sktime.transformations.panel.tsfresh import TSFreshFeatureExtractor, TSFreshRelevantFeatureExtractor
  from sktime.utils.warnings import warn
label_desc: Use image node_id 4 for calling the TSFreshClassifier obj's underlying member method code with example usage: obj._predict_proba(X) and returns: dists, self, dists