From 5eb9b16a7cba5334486ccd64b905f5f7ab15a480 Mon Sep 17 00:00:00 2001 From: kwinkunks Date: Sat, 9 Dec 2023 16:14:41 +0100 Subject: [PATCH] Fixing review issues Fixes #89 --- paper/paper.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index d7c7ef6..36b7cb7 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -96,7 +96,7 @@ method on existing Pandas objects, e.g. `df['target'].redflag.is_imbalanced()`. 3. **Standalone functions** which the user can compose their own checks and tests with, e.g. `redflag.is_imbalanced(y)`. -There are two kinds of `scikit-learn` transformer: +The `scikit-learn` transformers are of two kinds: - **Detectors** check every dataset they encounter. For example, `redflag.ClippingDetector` checks for clipped data during both model fitting @@ -121,8 +121,10 @@ services, and a correspondingly large API. Meanwhile, [`pandera`](https://github.com/unionai-oss/pandera), [`pandas-profiling`](https://github.com/ydataai/ydata-profiling) are all oriented around Pandas, Spark or other DataFrame-like structures. Finally, -[`evidently`](https://github.com/evidentlyai/evidently) provides on a Jupyter -interface with lots of plots. +[`evidently`](https://github.com/evidentlyai/evidently) provides a graphical +dashboard for Jupyter. In comparison, _Redflag_ is easier to set up and use +than `great_expectations` and `pandera`, and while it is compatible with +Pandas DataFrames and Jupyter it does not depend on them. By providing to machine learning practitioners a range of alerts and alarms, each of which can easily be inserted into existing workflows and pipelines,