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shapash - 🔅 Shapash makes Machine Learning models transparent and understandable by everyone
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.
machine-learning transparency lime interpretability ethical-artificial-intelligence explainable-ml shap explainabilityMindsDB - In-Database Machine Learning
MindsDB enables you to use ML predictions in your database using SQL. MindsDB automates and abstracts machine learning models through virtual AI Tables. It can easily make predictions over very complex multivariate time-series data with high cardinality.
machine-learning clickhouse postgresql ml snowflake mariadb pytorch artificial-intelligence machine-learning-api hacktoberfest automl explainable-ai explainable-ml singlestorexai - XAI - An eXplainability toolbox for machine learning
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.
machine-learning ai evaluation ml artificial-intelligence upsampling bias interpretability feature-importance explainable-ai explainable-ml xai imbalance downsampling explainability bias-evaluation machine-learning-explainability xai-libraryAIX360 - Interpretability and explainability of data and machine learning models
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.
explainable-ai explainable-ml trusted-ai trusted-ml machine-learning deep-learning codait artificial-intelligence explainabil xai ibm-research ibm-research-aiawesome-machine-learning-interpretability - A curated list of awesome machine learning interpretability resources
A curated, but probably biased and incomplete, list of awesome machine learning interpretability resources. If you want to contribute to this list (and please do!) read over the contribution guidelines, send a pull request, or contact me @jpatrickhall.
fairness xai interpretability iml fatml accountability transparency machine-learning data-science data-mining r awesome awesome-list machine-learning-interpretability interpretable-machine-learning interpretable-ml interpretable-ai interpretable-deep-learning explainable-mltensorwatch - Debugging, monitoring and visualization for Deep Learning and Reinforcement Learning
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.
ai deep-learning deeplearning machine-learning machinelearning machinelearning-python reinforcement-learning debugging debugging-tool debugger-visualizer debug monitoring explainable-ai explainable-ml saliency salient-object-detection model-visualizationdiabetes_use_case - Sample use case for Xavier AI in Healthcare conference: https://www
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.
xgboost healthcare interpretability xai iml transparency machine-learning data-science data-mining machine-learning-interpretability interpretable-ml interpretable-machine-learning explainable-mlml-fairness-framework - FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
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.
contrastive counterfactual explainable-ai explainable-ml xai interpretable-machine-learning fairness-ai aif360 fairness-ml alibi prototypical aix360 fairness-indicatorscav-keras - Concept activation vectors for Keras
In this package, we allow a user to explore concept activation vectors CAVs in their Keras models. We provide a simple example using CIFAR data.
interpretability explainable-artificial-intelligence interpretable-deep-learning interpretable-ai interpretable-ml explainable-ai explainable-ml interpretable-machine-learning explainability
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