As Nachet Interactive progresses, a standard way to test and compare the performance of the various models used becomes necessary to provide good data and value to the data scientist. These automated tests will help them in their decision making developing new models for the application. The accuracy objective of the models described in our milestones is 90%, having these tests will help provide a good overview of the models and find the most performant one.
Steps by Steps :clipboard:
Defined the objectives of the test
Define relevant metrics
Prepare test data from the blob storage
Implement the automated tests
Execute tests
Documents and communicate
Produce test reports
Acceptance Criteria :white_check_mark:
All test data, including models' results, are recorded
The implemented data visualizer effectively presents the results of the tests, enhancing data analysis capabilities.
Tasks 🛠️
[ ] #18
[ ] Start the command line testing application for Nachet
[ ] Refactor Finesse functions into more general ones that can do work for Finesse and Nachets
[ ] Record all tests run
[ ] Builds tools to create report tests
[ ] Build and maintain testing documentation (Wiki, GitHub, etc.)
Description :rocket:
As Nachet Interactive progresses, a standard way to test and compare the performance of the various models used becomes necessary to provide good data and value to the data scientist. These automated tests will help them in their decision making developing new models for the application. The accuracy objective of the models described in our milestones is 90%, having these tests will help provide a good overview of the models and find the most performant one.
Steps by Steps :clipboard:
Acceptance Criteria :white_check_mark:
Tasks 🛠️