class_docstr: Base time series forest classifier.
class_docstr_tok: ["Base","time","series","forest","classifier","."]
code: def __init__(
      self,
      min_interval=3,
      n_estimators=200,
      n_jobs=1,
      inner_series_length: Optional[int] = None,
      random_state=None,
  ):
      super().__init__(
          self._base_estimator,
          n_estimators=n_estimators,
      )
  
      self.random_state = random_state
      self.n_estimators = n_estimators
      self.min_interval = min_interval
      self.n_jobs = n_jobs
      self.inner_series_length = inner_series_length
      # The following set in method fit
      self.n_classes = 0
      self.series_length = 0
      self.n_intervals = 0
      self.estimators_ = []
      self.intervals_ = []
      self.classes_ = []
  
      # We need to add is-fitted state when inheriting from scikit-learn
      self._is_fitted = False
  
file_path: sktime/sktime/series_as_features/base/estimators/interval_based/_tsf.py
imports: import math
  from typing import Optional
  import numpy
  from joblib import Parallel, delayed
  from numpy.random import RandomState
  from sklearn.utils.multiclass import class_distribution
  from sklearn.utils.validation import check_random_state
  from sktime.base._base import _clone_estimator
  from sktime.utils.slope_and_trend import _slope
  from sktime.utils.validation import check_n_jobs
label_desc: Use image node_id 1 to create a new BaseTimeSeriesForest object  with example: obj = BaseTimeSeriesForest(min_interval, n_estimators, n_jobs, inner_series_length, random_state)