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Gain insight, debug, and improve ML models with InterpretML. Its interactive visualizations and tools make analysis easy. Perfect for data professionals.

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What is InterpretML?

InterpretML is an advanced machine learning (ML) platform that helps data scientists, ML engineers, and developers gain valuable insights into their ML models. With its powerful capabilities, this platform allows users to effortlessly understand, debug, and enhance their models. Offering a range of interactive visualizations and metrics, InterpretML enables users to swiftly analyze their model's performance and identify areas for potential improvement. Additionally, it provides a comprehensive set of tools for debugging and monitoring, such as feature importance, partial dependence plots, and instance-level explanations. What sets InterpretML apart is its user-friendly interface, making it simple for anyone, including beginners, to comprehend and analyze complex ML models effectively. Whether you are an experienced data professional or a novice, InterpretML is the ideal choice for gaining a deeper understanding of your ML models. Its advanced features and interactive visualizations empower users to easily debug, monitor, and enhance their models, while its intuitive design ensures accessibility for all.


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InterpretML FQA

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InterpretML Use Cases

Model interpretability helps developers, data scientists and business stakeholders in the organization gain a comprehensive understanding of their machine learning models. It can also be used to debug models, explain predictions and enable auditing to meet compliance with regulatory requirements.

Access state-of-the-art interpretability techniques through an open unified API set and rich visualizations.

Understand models using a wide range of explainers and techniques using interactive visuals. Choose your algorithm and easily experiment with combinations of algorithms.

Explore model attributes such as performance, global and local features and compare multiple models simultaneously. Run what-if analysis as you manipulate data and view the impact on the model.

Glass-box models are interpretable due to their structure. Examples include: Explainable Boosting Machines (EBM), Linear models, and decision trees.

Black-box models are challenging to understand, for example deep neural networks. Black-box explainers can analyze the relationship between input features and output predictions to interpret models. Examples include LIME and SHAP.

Explore overall model behavior and find top features affecting model predictions using global feature importance

Explain an individual prediction and find features contributing to it using local feature importance

Explain a subset of predictions using group feature importance

See how changes to input features impact predictions with techniques like what-if analysis

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