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 📈 in explaining model predictions to business stakeholders and building trustworthy AI solutions. 💪

Our mission is to make a user-friendly, intuitive, and collaborative product for data science teams to enable explainable and resonsible AI. 🙏

With explainX, you can:

• ​👩🏻💻​👨🏽💻 Explain model predictions by identifying important features and their impact on the final outcome.

• ​🔁Debug machine learning models by understanding data distributions and the marginal effects of changing individual feature values.

• ​👩💻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.

What's more?

Get in touch with us now: https://www.explainx.ai/contact-us​

Check out our pricing: https://www.explainx.ai/pricing​

​Join our Slack community: https://www.explainx.ai/join-our-slack-community​