Machine-Learning
DALEX
| Machine-Learning | DALEX | |
|---|---|---|
| 2 | 2 | |
| 86 | 1,326 | |
| - | 0.8% | |
| 3.4 | 5.9 | |
| 5 months ago | 2 days ago | |
| Python | Python | |
| - | GNU General Public License v3.0 only |
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Machine-Learning
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I published a Free & Open Source book to Learn Python 3. It includes a nice website for online reading and PDF for offline reading. Any feedback is highly appreciated.
Thank you for sharing! Am I the only one who never learned tu-ples, lists, dictionaries, arrays and so on yet able to write some rather sophisticated Python code without really understanding the data structures that I use? See my GitHub repository at https://github.com/VincentGranville/Machine-Learning, full of Python code. I play with data structures the same way I play with grammar in English: I do it successfully, without knowing the rules or the inner workings.
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My New Machine Learning Dictionary: Which Terms Would You Add?
Top entries are in bold, and sub-entries are in italics. This dictionary is from my new book “Intuitive Machine Learning and Explainable AI”, available here and used as reference material for the course with the same name (see here). These entries are cross-referenced in the book to facilitate navigation, with backlinks to the pages where they appear. The index, also with clickable backlinks, is a more comprehensive listing with 300+ terms. Both the glossary and index are available in PDF format here on my GitHub repository, and of course with clickable links within the book.
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.
What are some alternatives?
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
captum - Model interpretability and understanding for PyTorch
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
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.
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
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.
interpret - Fit interpretable models. Explain blackbox machine learning.
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
sagemaker-explaining-credit-decisions - Amazon SageMaker Solution for explaining credit decisions.
EthicML - Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency
alibi - Algorithms for explaining machine learning models
AIX360 - Interpretability and explainability of data and machine learning models