shap VS ML-Prediction-LoL

Compare shap vs ML-Prediction-LoL and see what are their differences.

shap

A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap] (by slundberg)

ML-Prediction-LoL

In this project I implemented two machine learning algorithms to predicts the outcome of a League of Legends game. (by reneleogp)
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shap ML-Prediction-LoL
1 2
20,121 43
- -
10.0 0.0
8 months ago over 1 year ago
Jupyter Notebook Jupyter Notebook
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.

shap

Posts with mentions or reviews of shap. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-18.

ML-Prediction-LoL

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

What are some alternatives?

When comparing shap and ML-Prediction-LoL you can also consider the following projects:

csgo-impact-rating - A probabilistic player rating system for Counter Strike: Global Offensive, powered by machine learning

reinforcement_learning_course_materials - Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University

transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

lolesports-predictor - Personal machine learning & GUI project to predict League of Legends Esports game results between two teams

lime - Lime: Explaining the predictions of any machine learning classifier

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.

Deep_Learning_Machine_Learning_Stock - Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

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