class_docstr: Random Interval Classifier.
  
  This classifier simply transforms the input data using the RandomIntervals
  transformer and builds a provided estimator using the transformed data.
  
  Parameters
  ----------
  n_intervals : int, default=100,
      The number of intervals of random length, position and dimension to be
      extracted.
  interval_transformers : transformer or list of transformers, default=None,
      Transformer(s) used to extract features from each interval. If None, defaults to
      the Catch22 transformer.
  estimator : sklearn classifier, default=None
      An sklearn estimator to be built using the transformed data. Defaults to a
      Rotation Forest with 200 trees.
  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 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
  --------
  RandomIntervals
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code: def __init__(
      self,
      n_intervals=100,
      interval_transformers=None,
      estimator=None,
      n_jobs=1,
      random_state=None,
  ):
      self.n_intervals = n_intervals
      self.interval_transformers = interval_transformers
      self.estimator = estimator
  
      self.n_jobs = n_jobs
      self.random_state = random_state
  
      self._transformer = None
      self._estimator = None
  
      super().__init__()
  
file_path: sktime/sktime/classification/feature_based/_random_interval_classifier.py
imports: import numpy
  from sktime.base._base import _clone_estimator
  from sktime.classification.base import BaseClassifier
  from sktime.classification.sklearn import RotationForest
  from sktime.transformations.panel.catch22 import Catch22
  from sktime.transformations.panel.random_intervals import RandomIntervals
label_desc: Use image node_id 1 to create a new RandomIntervalClassifier object from inherited base classes: BaseClassifier with example: obj = RandomIntervalClassifier(n_intervals, interval_transformers, estimator, n_jobs, random_state)