ExplainX is an explainable AI library for data scientists that enables them to explain, analyze and debug any black-box machine learning model - in just a single line of code.
We want data scientists to be more effective
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in explaining model predictions to business stakeholders and building trustworthy AI solutions.
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Our mission is to make a user-friendly, intuitive, and collaborative product for data science teams to enable explainable and resonsible AI.
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With explainX, you can:
👩🏻💻👨🏽💻 Explain model predictions by identifying important features and their impact on the final outcome.
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Debug machine learning models by understanding data distributions and the marginal effects of changing individual feature values.
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Use the SQL module to dive into how model behavior changes with different subsets of your dataset. For example, the model might behave differently for different categories.
🎨 Business explanations by identifying similar profiles that support your model logic and are understood easily by your business stakeholder.
🎨 Use our what-if analysis and counterfactuals to play out different business scenarios and identify actionable recommendations for better business decision making.