fairgbm VS interpret

Compare fairgbm vs interpret and see what are their differences.

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fairgbm interpret
2 6
92 5,782
- 0.7%
0.0 5.5
2 months ago 8 days ago
C++ C++
GNU General Public License v3.0 or later 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.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.


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

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


Posts with mentions or reviews of interpret. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-11-25.

What are some alternatives?

When comparing fairgbm and interpret you can also consider the following projects:

shap - A game theoretic approach to explain the output of any machine learning model.

shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

alibi - Algorithms for explaining machine learning models

imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

medspacy - Library for clinical NLP with spaCy.

decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest

DashBot-3.0 - Geometry Dash bot to play & finish levels - Now training much faster!

AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

DALEX - moDel Agnostic Language for Exploration and eXplanation

sagemaker-explaining-credit-decisions - Amazon SageMaker Solution for explaining credit decisions.

yggdrasil-decision-forests - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.