User Testing Instructions
We are looking for people to help us Alpha test the Yellowbrick project! Helping is simple: simply create a notebook that applies the concepts in this Getting Started guide to a small-to-medium size dataset of your choice. Run through the examples with the dataset, and try to change options and customize as much as possible. After you’ve exercised the code with your examples, respond to our alpha testing survey!
Step One: Questionnaire
Please open the questionnaire, in order to familiarize yourself with the type of feedback we are looking to receive. We are very interested in identifying any bugs in Yellowbrick. Please include any cells in your Jupyter notebook that produce errors so that we may reproduce the problem.
Step Two: Dataset
Select a multivariate dataset of your own. The greater the variety of datasets that we can run through Yellowbrick, the more likely we’ll discover edge cases and exceptions! Please note that your dataset must be well-suited to modeling with scikit-learn. In particular, we recommend choosing a dataset whose target is suited to one of the following supervised learning tasks:
There are datasets that are well suited to both types of analysis; either way, you can use the testing methodology from this notebook for either type of task (or both). In order to find a dataset, we recommend you try the following places:
You’re more than welcome to choose a dataset of your own, but we do ask that you make at least the notebook containing your testing results publicly available for us to review. If the data is also public (or you’re willing to share it with the primary contributors) that will help us figure out bugs and required features much more easily!
Step Three: Notebook
Create a notebook in a GitHub repository. We suggest the following:
Fork the Yellowbrick repository
examplesdirectory, create a directory named with your GitHub username
Create a notebook named
testing, i.e. examples/USERNAME/testing.ipynb
Alternatively, you could just send us a notebook via Gist or your own repository. However, if you fork Yellowbrick, you can initiate a pull request to have your example added to our gallery!
Step Four: Model with Yellowbrick and Scikit-Learn
Add the following to the notebook:
A title in markdown
A description of the dataset and where it was obtained
A section that loads the data into a Pandas dataframe or NumPy matrix
Then conduct the following modeling activities:
Feature analysis using scikit-learn and Yellowbrick
Estimator fitting using scikit-learn and Yellowbrick
You can follow along with our
examples directory (check out examples.ipynb) or even create your own custom visualizers! The goal is that you create an end-to-end model from data loading to estimator(s) with visualizers along the way.
IMPORTANT: please make sure you record all errors that you get and any tracebacks you receive for step three!
Step Five: Feedback
Finally, submit feedback via the Google Form we have created:
This form is allowing us to aggregate multiple submissions and bugs so that we can coordinate the creation and management of issues. If you are the first to report a bug or feature request, we will make sure you’re notified (we’ll tag you using your Github username) about the created issue!
Step Six: Thanks!
Thank you for helping us make Yellowbrick better! We’d love to see pull requests for features you think should be added to the library. We’ll also be doing a user study that we would love for you to participate in. Stay tuned for more great things from Yellowbrick!