From a77c5ce188a037899ba79d83bbda7cf8d639792a Mon Sep 17 00:00:00 2001 From: kwinkunks Date: Wed, 27 Sep 2023 21:42:01 +0200 Subject: [PATCH] Update changelog --- CHANGELOG.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 27ed00b..54c856e 100644 --- a/CHANGELOG.md +++ b/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 M2 records, where M is the number of features in `X`. +- Added `redflag.sklearn.InsufficientDataDetector` which checks that there are at least M2 records (rows in `X`), where M is the number of features (i.e. columns) in `X`. - Removed `RegressionMultimodalDetector`. Use `MultimodalDetector` instead.