v0.1.10

• Added redflag.importance.least_important_features() and redflag.importance.most_important_features(). These functions are complementary (in other words, if the same threshold is used in each, then between them they return all of the features). The default threshold for importance is half the expected value. E.g. if there are 5 features, then the default threshold is half of 0.2, or 0.1. Part of Issue 2.
• Added redflag.sklearn.ImportanceDetector class, which warns if 1 or 2 features have anomalously high importance, or if some features have anomalously low importance. Part of Issue 2.
• Added redflag.sklearn.ImbalanceComparator class, which learns the imbalance present in the training data, then compares what is observed in subsequent data (evaluation, test, or production data). If there's a difference, it throws a warning. Note: it does not warn if there is imbalance present in the training data; use ImbalanceDetector for that.
• Added redflag.sklearn.RfPipeline class, which is needed to include the ImbalanceComparator in a pipeline (because the common-or-garden sklearn.pipeline.Pipeline class does not pass y into a transformer's transform() method). Also added the redflag.sklearn.make_rf_pipeline() function to help make pipelines with this special class. These components are straight-up forks of the code in scikit-learn (3-clause BSD licensed).
• Added example to docs/notebooks/Using_redflag_with_sklearn.ipynb to show how to use these new objects.
• Improved redflag.is_continuous(), which was buggy; see Issue 3. It still fails on some cases. I'm not sure a definitive test for continuousness (or, conversely, discreteness) is possible; it's just a heuristic.