shap
csgo-impact-rating
shap | csgo-impact-rating | |
---|---|---|
1 | 1 | |
20,121 | 9 | |
- | - | |
10.0 | 0.0 | |
8 months ago | about 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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.
shap
-
Ethical and Bias Testing in Generative AI: A Practical Guide to Ensuring Ethical Conduct with Test Cases and Tools
Other tools like Fairness Indicators, Lime, and SHAP are also valuable resources for ethical and bias testing.
csgo-impact-rating
-
A simple round outcome predictor project
I did something similar a couple of years ago using gradient boosting to play around with an "impact" based player rating system, was a lot of fun to work on: https://github.com/phil-holland/csgo-impact-rating
What are some alternatives?
transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
ML-Prediction-LoL - In this project I implemented two machine learning algorithms to predicts the outcome of a League of Legends game.
lime - Lime: Explaining the predictions of any machine learning classifier
CSGO-Pro-Gear-Performance-and-EDA - Modeling Professional (CS:GO) Gamer's Accuracy Performance Based on Gear and Settings, and Exploratory Data Analysis.
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
streamlit - Streamlit — A faster way to build and share data apps.
augmented-interpretable-models - Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
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
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning