https://github.com/holistic-ai/holisticai - Release 1.0.14

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https://github.com/holistic-ai/holisticai - Release 1.0.13

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https://github.com/holistic-ai/holisticai - Release 1.0.12

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https://github.com/holistic-ai/holisticai - Release 1.0.11

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https://github.com/holistic-ai/holisticai - Release 1.0.10

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https://github.com/holistic-ai/holisticai - Release 1.0.9

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https://github.com/holistic-ai/holisticai - Release 1.0.8

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https://github.com/holistic-ai/holisticai - Release 1.0.7

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https://github.com/holistic-ai/holisticai - Release 1.0.6

🚀 Features Adding Surrogate and Tree-Based Metrics (#225) Adding regression attacker LGB poisoner (#220)

⚙️ Miscellaneous Tasks Update files for new gitflow (#227) (#228)

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https://github.com/holistic-ai/holisticai - Release 1.0.5

🚀 Features Tree based metrics for explainability (#210) Implement accuracy degradation profile (#211)

🐛 Bug Fixes Single output dataset (#212)

⚙️ Miscellaneous Tasks Remove duplicated test running (#215) Improving bias mitigators and Refactor Dataset Object (#213)

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https://github.com/holistic-ai/holisticai - Release 1.0.4

📚 Documentation Update xai tutorials (#209)

⚙️ Miscellaneous Tasks Enhance surrogate feature importance with multiples Decision Trees (#208)

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https://github.com/holistic-ai/holisticai - Release 1.0.3

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https://github.com/holistic-ai/holisticai - Release 1.0.2

🐛 Bug Fixes

• Remove sklearn parallel dependency (#196)

📚 Documentation

• Update robustness documentation (#194)
• Adding tutorials (#195)

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https://github.com/holistic-ai/holisticai - Release 1.0.1

🚀 Features

• Add metrics for robustness in binary classification (#191)

⚙️ Miscellaneous Tasks

• Update Dataset documentation and remove unused dependencies. (#192)
• Update slack message (#193)

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https://github.com/holistic-ai/holisticai - Release 1.0.0

• Accelerate Bias Mitigators Training: Utilize JAX, JAXopt, and Flax to enhance the speed and efficiency of pre-training, in-training, and post-training bias mitigation techniques.

• Enhance Security Metrics: Introduce more comprehensive security metrics to ensure the project meets high-security standards.

• Accelerate Explainability Metrics: Improve the computation and visualization of explainability metrics to make model predictions more transparent and understandable.

• Improved Dataset Handling: Implement a Dataset object that more effectively manages bias in tabular datasets, ensuring better handling and analysis of biased data.

• Improved Documentation: Provide comprehensive and user-friendly documentation to assist developers in understanding and utilizing the project's features and functionalities.

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