shapash and xai
These tools are competitors, as both Shapash and EthicalML/xai offer frameworks and toolboxes for generating explanations and interpretations of machine learning models.
About shapash
MAIF/shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
This project helps data scientists and machine learning engineers understand why their predictive models make certain decisions. It takes a trained machine learning model and its input data, then generates easy-to-understand visualizations and reports that explain the model's behavior. The output helps both technical and non-technical stakeholders gain trust and insights into the model's predictions.
About xai
EthicalML/xai
XAI - An eXplainability toolbox for machine learning
This tool helps data scientists and machine learning engineers analyze and evaluate their machine learning models to ensure fairness and transparency. It takes in your dataset and trained model, then outputs visualizations and metrics that highlight data imbalances, feature importance, and model performance across different groups. This is for anyone building or deploying machine learning models who needs to understand why their model makes certain decisions and identify potential biases.
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