- MICROSOFT TOOLKIT 2.4.5 INSTALLSLIC32ON64 ERROR HOW TO
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If you intend to run repository tests, in the raiwidgets folder of the repository run: pip install -r requirements.txt Getting started You will need to install yarn and node to build the visualization code, and then you can run: yarn installĪnd install from the raiwidgets folder locally: cd raiwidgetsįor more information see the contributing guide.
MICROSOFT TOOLKIT 2.4.5 INSTALLSLIC32ON64 ERROR CODE
pip install interpret-communityĪlternatively, you can also clone the open source repository and build the code from scratch: git clone If you do not have interpret-community already installed, you will also need to install this for supporting the generation of model explanations. To install the Responsible AI Widgets “raiwidgets” package, in your python environment simply run the following to install the raiwidgets package from pypi. If you are interested in learning more about training model updates that remain backward compatible with their previous selves by minimizing regress and new errors, you can also check out our most recent open source library and tool BackwardCompatibilityML. Not only a contribution to the OSS RAI community, but practitioners can also leverage these assessment tools in Azure Machine Learning, including Fairlearn & InterpretML and now Error Analysis in mid 2021. The Error Analysis toolkit is integrated within the Responsible AI Widgets OSS repository, our starting point to provide a set of integrated tools to the open source community and ML practitioners.
Debugging ML errors with active data exploration and interpretability techniques.Getting a deep understanding of how failure is distributed for a model.
MICROSOFT TOOLKIT 2.4.5 INSTALLSLIC32ON64 ERROR HOW TO
To accelerate rigorous ML development, in this blog you will learn how to use the Error Analysis tool for: To address these problems, practitioners often have to create custom infrastructure, which is tedious and time-consuming. In addition, in the longer term, when models are updated and re-deployed frequently upon new data evidence or scientific progress, teams also need to continuously track and monitor model behavior so that updates do not introduce new mistakes and therefore break user trust. Navigating the terrain of failures along multiple potential dimensions like the above can be challenging. At the same time, there may exist several dimensions of the input feature space that a practitioner may be interested in taking a deep dive and ask questions such as “What happens to the accuracy of the recognition model in a self-driving car when it is dark and snowing outside?” or “Does the loan approval model perform similarly for population cohorts across ethnicity, gender, age, and education?”. It is difficult to convey a detailed story on model behavior with a single number and yet most of the research and leaderboards operate on single scores. While there exist several problems with current model assessment practices, one of the most obvious is the usage of aggregate metrics to score models on a whole benchmark. For instance, when a traffic sign detector does not operate well in certain daylight conditions or for unexpected inputs, even though the overall accuracy of the model may be high, it is still important for the development team to know ahead of time about the fact that the model may not be as reliable in such situations.įigure 1 - Error Analysis moves away from aggregate accuracy metrics, exposes the distribution of errors to developers in a transparent way, and enables them to identify & diagnose errors efficiently. Often, such failures may cause direct consequences related to lack of reliability and safety, unfairness, or more broadly lack of trust in machine learning altogether. How often do we read claims such as “Model X is 90% on a given benchmark.” and wonder what does this claim mean for practical usage of the model? In practice, teams are well aware that model accuracy may not be uniform across subgroups of data and that there might exist input conditions for which the model fails more often. Machine Learning (ML) teams who deploy models in the real world often face the challenges of conducting rigorous performance evaluation and testing for ML models.