Skip to content

adding badges to the test workflow #6

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Feb 16, 2021
Merged

adding badges to the test workflow #6

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
3d5904c
ci: added badges
iam-kevin Feb 16, 2021
cf3178b
docs: cahnge badge to 'dev'
iam-kevin Feb 16, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion .github/workflows/main.yml
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
name: Test + Coveralls
name: ci-test-coverage
on:
push:
branches:
Expand Down
5 changes: 3 additions & 2 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,7 +1,8 @@
![CI](https://github.com/Elsa-Health/bayesian-logistic-regressor/workflows/CI/badge.svg?branch=main)

# Bayesian Logistic Regression

![CI](https://github.com/Elsa-Health/bayesian-logistic-regressor/workflows/ci-test-coverage/badge.svg?branch=dev)
[![Coverage Status](https://coveralls.io/repos/github/Elsa-Health/bayesian-logistic-regressor/badge.svg?branch=test/init-workflow)](https://coveralls.io/github/Elsa-Health/bayesian-logistic-regressor?branch=test/init-workflow)

According to Wikipedia:

> In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object being detected in the image would be assigned a probability between 0 and 1, with a sum of one.
Expand Down