DALEX
pyGAM
| DALEX | pyGAM | |
|---|---|---|
| 2 | 2 | |
| 1,323 | 839 | |
| 0.6% | - | |
| 5.5 | 2.4 | |
| 2 months ago | 20 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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DALEX
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Twitter set to accept ‘best and final offer’ of Elon Musk
Which he will not do, because: a) He can't, it's a black box algorithm. It actually is open source already, but that doesn't mean much as it's useless without Twitter's data https://github.com/ModelOriented/DALEX b) He won't release data that shows the algorithm is racist and amplifies conservative and extremist content. He won't remove such functions because it will cost him billions.
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[D] What are your favorite Random Forest implementations that support categoricals
There are a couple of ways to use Shapley values for explanations in R. One way is to use DALEX, which also contains a lot of other methods besides SHAP. Another one is iml. I am sure there are several other implementations of SHAP as well.
pyGAM
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[P] glum: High performance Python generalized linear modeling, a glmnet alternative!
GLMs are nice and all, but I'd also like to see some love for GAMs. pygam looks pretty much dead for some years now.
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[P] Which Machine Learning Classifiers are best for small datasets? An empirical study
Ah they haven't quite gotten around to supporting multiclass classification yet! https://github.com/dswah/pyGAM/pull/213
What are some alternatives?
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
scikit-learn - scikit-learn: machine learning in Python
captum - Model interpretability and understanding for PyTorch
optuna - A hyperparameter optimization framework
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
glum - High performance Python GLMs with all the features!
responsible-ai-toolbox - Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Empirical_Study_of_Ensemble_Learning_Methods - Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
tabmat - Efficient matrix representations for working with tabular data
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation