ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.
The paper has been published at ACM CHI 2019.PDF
git clone https://github.com/HDI-Project/ATMSeer.git
Then go to ATMSeer project from the terminal and run
This will install all the necessary packages in a virtual environment. After the installation finishes, run
http://localhost:7779/ at your web broswer to see the ATMSeer.
public/viz/, add Dataruns
(+ button) and hit Run
There are small issues at first run:
At first upload step, couple console errors will be present - ignore them Go to terminal and run
After VM is up and running, go to the browser, refresh the page, and from the
Dataset dropdown select
blood as dataset and hit the run button.
At this step, you should be able to see HyperPartitions and HyperParameters of selected alghoritm