Repository: sktime/sktime

Issue:
[DOC] Broken documentation on `RandomIntervalSpectralForest`
#### Describe the issue linked to the documentation

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Hi, thank you for contributing to this nice repo. 

I find that the documentation page on `RandomIntervalSpectralForest` is broken (https://www.sktime.org/en/latest/modules/auto_generated/sktime.classification.frequency_based.RandomIntervalSpectralForest.html). This is what it looks like:

![screen](https://user-images.githubusercontent.com/22359569/98362893-fab84500-2068-11eb-8ebf-9bba6a9ca1b5.png)

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Discussion:
@AaronX121 Thanks for raising this! Would appreciate a PR :) 
Got it, I am happy to take this, and open a PR latter.
Thanks @AaronX121, really appreciated! 

Resolving patch:
diff --git a/sktime/classification/frequency_based/_rise.py b/sktime/classification/frequency_based/_rise.py
--- a/sktime/classification/frequency_based/_rise.py
+++ b/sktime/classification/frequency_based/_rise.py
@@ -22,9 +22,9 @@ class RandomIntervalSpectralForest(ForestClassifier, BaseClassifier):
     """Random Interval Spectral Forest (RISE).
 
     Random Interval Spectral Forest: stripped down implementation of RISE
-    from Lines 2018:
-    @article
-    {lines17hive-cote,
+
+    from Lines 2018::
+        @article{lines17hive-cote,
      author = {J. Lines, S. Taylor and A. Bagnall},
      title = {Time Series Classification with HIVE-COTE: The
       Hierarchical Vote Collective of Transformation-Based Ensembles},
@@ -35,43 +35,48 @@ class RandomIntervalSpectralForest(ForestClassifier, BaseClassifier):
     }
     https://dl.acm.org/doi/10.1145/3182382
 
-    Overview: 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.
-    Need to have a minimum interval for each tree
-    This is from the python github.
+    Overview:
 
+    .. code-block:: none
 
+        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.
+
+    Need to have a minimum interval for each tree
+    This is from the python github.
 
     Parameters
     ----------
-    n_estimators         : int, ensemble size, optional (default = 200)
-    random_state    : int, seed for random, integer, optional (default to no
-    seed)
-    min_interval    : int, minimum width of an interval, optional (default =
-    16)
-    acf_lag         : int, maximum number of autocorrelation terms to use (
-    default =100)
-    acf_min_values  : int, never use fewer than this number of terms to find
-    a correlation (default =4)
+    n_estimators : int, optional (default=200)
+        The number of trees in the forest.
+    random_state : int, RandomState instance or None, optional (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`.
+    min_interval : int, optional (default=16)
+        The minimum width of an interval.
+    acf_lag : int, optional (default=100)
+        The maximum number of autocorrelation terms to use.
+    acf_min_values : int, optional (default=4)
+        Never use fewer than this number of terms to find a correlation.
 
     Attributes
     ----------
-    n_classes    : int, extracted from the data
-    classifiers    : array of shape = [n_estimators] of DecisionTree
-    classifiers
-    intervals      : array of shape = [n_estimators][2] stores indexes of
-    start
-    and end points for all classifiers
-
-    TO DO: handle missing values, unequal length series and multivariate
-    problems
-
+    n_classes : int
+        The number of classes, extracted from the data.
+    classifiers : array of shape = [n_estimators] of DecisionTree classifiers
+    intervals : array of shape = [n_estimators][2]
+        Stores indexes of start and end points for all classifiers.
     """
 
+    # TO DO: handle missing values, unequal length series and multivariate
+    # problems
+
     def __init__(
         self,
         n_estimators=200,


Test patch: