class_docstr: Random Interval Spectral Ensemble (RISE).
  
  Input: n series length m
  For each tree
      - sample a random intervals
      - take the ACF and PS over this interval, and concatenate features
      - build tree on new features
  Ensemble the trees through averaging probabilities.
  
  Parameters
  ----------
  n_estimators : int, default=200
      The number of trees in the forest.
  min_interval : int, default=16
      The minimum width of an interval.
  acf_lag : int, default=100
      The maximum number of autocorrelation terms to use.
  acf_min_values : int, default=4
      Never use fewer than this number of terms to find a correlation.
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  random_state : int, RandomState instance or None, default=None
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  Attributes
  ----------
  n_classes_ : int
      The number of classes.
  classes_ : list
      The classes labels.
  intervals_ : array of shape = [n_estimators][2]
      Stores indexes of start and end points for all classifiers.
  
  Notes
  -----
  For the Java version, see
  `TSML <https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/
  classifiers/interval_based/RISE.java>`_.
  
  References
  ----------
  .. [1] Jason Lines, Sarah Taylor and Anthony Bagnall, "Time Series Classification
     with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based
     Ensembles", ACM Transactions on Knowledge and Data Engineering, 12(5): 2018
docstr: Find predictions for all cases in X.
  
  Built on top of `predict_proba`.
  
  Parameters
  ----------
  X : array-like or sparse matrix of shape = [n_instances, n_columns]
      The input samples. If a Pandas data frame is passed it must have a
      single column (i.e., univariate classification). RISE has no
      bespoke method for multivariate classification as yet.
  
  Returns
  -------
  y : array of shape = [n_instances]
      The predicted classes.
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code: def _predict(self, X) -> np.ndarray:
      """Find predictions for all cases in X.
  
      Built on top of `predict_proba`.
  
      Parameters
      ----------
      X : array-like or sparse matrix of shape = [n_instances, n_columns]
          The input samples. If a Pandas data frame is passed it must have a
          single column (i.e., univariate classification). RISE has no
          bespoke method for multivariate classification as yet.
  
      Returns
      -------
      y : array of shape = [n_instances]
          The predicted classes.
      """
      proba = self._predict_proba(X)
      return np.asarray(
          [self.classes_[np.argmax(prob)] for prob in proba]
      )
  
imports: import numpy
  from joblib import Parallel, delayed
  from sklearn.base import clone
  from sklearn.ensemble._base import _partition_estimators
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils.validation import check_random_state
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 4 for calling the RandomIntervalSpectralEnsemble obj's underlying member method code with example usage: obj._predict(X) and returns: np