Update changelog

CHANGELOG.md

@@ -1,13 +1,13 @@
 # Changelog
 
-## 0.3.1, coming Fall 2023
+## 0.3.1, 28 September 2023
 
 - `redflag` can now be installed by the `conda` package and environment manager. To do so, use `conda install -c conda-forge redflag`.
 - All of the `sklearn` components can now be instantiated with `warn=False` in order to trigger a `ValueException` instead of a warning. This allows you to build pipelines that will break if a detector is triggered.
-- You can now pass `groups` to `redflag.distributions.is_multimodal()`. If present, the modality will be checked for each group, returning a Boolean array of values (one for each group). This allows you to check a feature partitioned by target class, for example.
 - Added `redflag.target.is_ordered()` to check if a single-label categorical target is ordered in some way. The test uses a Markov chain analysis, applying chi-squared test to the transition matrix. In general, the Boolean result should only be used on targets with several classes, perhaps at least 10. Below that, it seems to give a lot of false positives.
+- You can now pass `groups` to `redflag.distributions.is_multimodal()`. If present, the modality will be checked for each group, returning a Boolean array of values (one for each group). This allows you to check a feature partitioned by target class, for example.
 - Added `redflag.sklearn.MultimodalityDetector` to provide a way to check for multimodal features. If `y` is passed and is categorical, it will be used to partition the data and modality will be checked for each class.
-- Added `redflag.sklearn.InsufficientDataDetector` which checks that there are at least M<sup>2</sup> records, where M is the number of features in `X`.
+- Added `redflag.sklearn.InsufficientDataDetector` which checks that there are at least M<sup>2</sup> records (rows in `X`), where M is the number of features (i.e. columns) in `X`.
 - Removed `RegressionMultimodalDetector`. Use `MultimodalDetector` instead.