Welcome!

This is a guide to Triage, a data science workflow tool initially developed at the Center for Data Science and Public Policy (DSaPP) at the University of Chicago and now being maintained at Carnegie Mellon University.

Triage helps build models for two common applied problems: (a) Early warning systems (EWS or EIS), (b) resource prioritization (a.k.a "an inspections problem") . These problems are difficult to model because their conceptualization and and implementation are prone to error, given their multi-dimensional, multi-entity, time-series structure.

What is in the name?#

There is a famous (and delicious) chinese duck restaurant in Chicago, we love that place, and as every restaurant in Chicago area, it gets inspected, so the naming is an homage to them.

Who is this tutorial for?#

We created this tutorial with two roles in mind:

• A data scientist/ML practitioner who wants to focus in the problem at his/her hands, not in the nitty-gritty detail about how to configure and setup a Machine learning pipeline, Model governance, Model selection, etc.

• A policy maker with a little of technical background that wants to learn how to pose his/her policy problem as a Machine Learning problem.

How to use this tutorial#

First, clone this repository on your laptop

git clone https://github.com/dssg/triage

Second, in the cloned repository's top-level directory run

./tutorial.sh up

This will take several minutes the first time you do it.

After this, you may decide to do the quickstart tutorial.

Before you start#

What you need for this tutorial#

Install Docker CE and Docker Compose. That's it! Follow the links for the installation instructions.

Note that if you are using GNU/Linux you should add your user to the docker group following the instructions at this link.

At the moment only operative systems with *nix-type command lines are supported, such as GNU/Linux and MacOS. Recent versions of Windows may also work.