Displaying 1 to 9 from 9 results

shapash - 🔅 Shapash makes Machine Learning models transparent and understandable by everyone

  •    Jupyter

Shapash is a Python library which aims to make machine learning interpretable and understandable by everyone. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can understand their models easily and share their results. End users can understand the decision proposed by a model using a summary of the most influential criteria.

xai - XAI - An eXplainability toolbox for machine learning

  •    Python

XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning. You can find the documentation at https://ethicalml.github.io/xai/index.html. You can also check out our talk at Tensorflow London where the idea was first conceived - the talk also contains an insight on the definitions and principles in this library.

mindsdb - Machine Learning in one line of code

  •    Python

MindsDB's is an Explainable AutoML framework for developers. MindsDB is an automated machine learning platform that allows anyone to gain powerful insights from their data. With MindsDB, users can get fast, accurate, and interpretable answers to any of their data questions within minutes.

AIX360 - Interpretability and explainability of data and machine learning models

  •    Python

The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available.




tensorwatch - Debugging, monitoring and visualization for Deep Learning and Reinforcement Learning

  •    Jupyter

TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning. It fully leverages Jupyter Notebook to show real time visualizations and offers unique capabilities to query the live training process without having to sprinkle logging statements all over. You can also use TensorWatch to build your own UIs and dashboards. In addition, TensorWatch leverages several excellent libraries for visualizing model graph, review model statistics, explain prediction and so on. TensorWatch is under heavy development with a goal of providing a research platform for debugging machine learning in one easy to use, extensible and hackable package.

diabetes_use_case - Sample use case for Xavier AI in Healthcare conference: https://www

  •    Jupyter

Recent advances enable practitioners to break open machine learning’s “black box”. From machine learning algorithms guiding analytical tests in drug manufacture, to predictive models recommending courses of treatment, to sophisticated software that can read images better than doctors, machine learning has promised a new world of healthcare where algorithms can assist, or even outperform, professionals in consistency and accuracy, saving money and avoiding potentially life-threatening mistakes. But what if your doctor told you that you were sick but could not tell you why? Imagine a hospital that hospitalized and discharged patients but was unable to provide specific justification for these decisions. For decades, this was a roadblock for the adoption of machine learning algorithms in healthcare: they could make data-driven decisions that helped practitioners, payers, and patients, but they couldn’t tell users why those decisions were made.

ml-fairness-framework - FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

  •    Jupyter

Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives. This is a holistic approach to implement fair outputs at the individual and group level. Some of the methods developed or used includes quantitative monotonic measures, residual explanations, benchmark competition, adverserial attacks, disparate error analysis, model agnostic pre-and post-processing, reasoning codes, counterfactuals, contrastive explanations, and prototypical examples.







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