csgo-impact-rating
shap
csgo-impact-rating | shap | |
---|---|---|
1 | 1 | |
9 | 20,121 | |
- | - | |
0.0 | 10.0 | |
about 1 year ago | 8 months 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.
csgo-impact-rating
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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
shap
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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.
What are some alternatives?
ML-Prediction-LoL - In this project I implemented two machine learning algorithms to predicts the outcome of a League of Legends game.
transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
CSGO-Pro-Gear-Performance-and-EDA - Modeling Professional (CS:GO) Gamer's Accuracy Performance Based on Gear and Settings, and Exploratory Data Analysis.
lime - Lime: Explaining the predictions of any machine learning classifier
streamlit - Streamlit — A faster way to build and share data apps.
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
shap - A game theoretic approach to explain the output of any machine learning model.
augmented-interpretable-models - Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
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