DiCE VS harakiri

Compare DiCE vs harakiri and see what are their differences.

DiCE

Generate Diverse Counterfactual Explanations for any machine learning model. (by interpretml)
counterfactual-explanations explainable-ml xai interpretable-machine-learning Deep Learning Machine Learning explainable-ai
Source Code
interpretml.github.io
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harakiri

Help applications kill themselves (by rubencaro)
Examples and funny stuff
Source Code
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DiCE harakiri
2 -
1,270 20
2.1% -
8.2 0.0
10 days ago over 6 years ago
Python Elixir
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

DiCE

Posts with mentions or reviews of DiCE. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-01-31.
  • [D] Have researchers given up on traditional machine learning methods?
    2 projects | /r/MachineLearning | 31 Jan 2023
    - all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications
  • [R] The Shapley Value in Machine Learning
    1 project | /r/MachineLearning | 25 Feb 2022
    Counter-factual and recourse-based explanations are alternative approach to model explanations. I used to work in a large financial institution, and we were researching whether counter-factual explanation methods would lead to better reason codes for adverse action notices.

harakiri

Posts with mentions or reviews of harakiri. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning harakiri yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing DiCE and harakiri you can also consider the following projects:

OmniXAI - OmniXAI: A Library for eXplainable AI

real world example app - Exemplary real world application built with Elixir + Phoenix

CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

phoenix-flux-react - An experiment with Phoenix Channels, GenEvents, React and Flux.

AIX360 - Interpretability and explainability of data and machine learning models

rubix - A very simple (and barely-functioning) Ruby runner for Elixir

interpret - Fit interpretable models. Explain blackbox machine learning.

elixir_koans - Elixir learning exercises

stranger - Chat anonymously with a randomly chosen stranger

magnetissimo - Web application that indexes all popular torrent sites, and saves it to the local database.

shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

koans - Small exercises to discover elixir by testing

DiCE vs OmniXAI harakiri vs real world example app DiCE vs CARLA harakiri vs phoenix-flux-react DiCE vs AIX360 harakiri vs rubix DiCE vs interpret harakiri vs elixir_koans DiCE vs stranger harakiri vs magnetissimo DiCE vs shapley harakiri vs koans