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<!DOCTYPE html>
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<p class="white text-center text-lg-left pb-3">A toolkit to help understand models and enable responsible machine learning</p>
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<p class="card-text gray-text masthead-text text-center px-sm-0 px-lg-5 pb-4">State-of-the-art techniques to explain model behavior</p>
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<div class="card h-100 masthead-card">
<img class="card-img-top mx-auto pt-5 active icon-blue" src="images/icon-02-blue.png">
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<p class="card-text gray-text masthead-text text-center pb-4">Comprehensive support for multiple types of models and algorithms, during training and inferencing</p>
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<p class="card-text gray-text masthead-text text-center px-sm-0 px-lg-5 pb-4">Community driven open source toolkit</p>
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<div id="why-interpret" class="container-fluid section-1 pb-5">
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<h2 class="pb-5 dark">Why InterpretML?</h2>
<h3 class="pb-3 dark">Model Interpretability</h3>
<p class="gray-text">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.</p>
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</div>
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<div class="card h-100 card-blue py-4">
<div class="row align-items-center">
<div class="col-12 col-lg-1 my-auto ml-lg-5">
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<div class="card-body text-center text-lg-left">
<h6 class="dark">Ease of use</h6>
<p class="gray-text card-text">Access state-of-the-art interpretability techniques through an open unified API set and rich visualizations.</p>
</div>
</div>
</div>
</div>
</div>
<div class="col-12 col-lg-7 mb-4">
<div class="card h-100 card-blue py-4">
<div class="row align-items-center">
<div class="col-12 col-lg-1 my-auto ml-lg-5">
<img class="card-icon mx-auto d-block" src="images/icon-05-blue.png">
</div>
<div class="col-lg-9 align-self-center align-self-lg-start">
<div class="card-body text-center text-lg-left">
<h6 class="dark">Flexible and customizable</h6>
<p class="gray-text card-text">Understand models using a wide range of explainers and techniques using interactive visuals. Choose your algorithm and easily experiment with combinations of algorithms.</p>
</div>
</div>
</div>
</div>
</div>
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</div>
<div class="col-lg-9 align-self-center align-self-lg-start">
<div class="card-body text-center text-lg-left">
<h6 class="dark">Comprehensive capabilities</h6>
<p class="gray-text card-text">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.</p>
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</div>
</div>
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<div class="container p-t-md">
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<h2 class="dark">Types of Models Supported</h2>
</div>
<div class="col-md-6">
<ul class="nav nav-tabs justify-content-between">
<li class="nav-item text-center">
<a class="nav-link pb-4 active" data-toggle="tab" href="#currentPreferences">
<img class="box-icons pt-5 pb-4 mx-auto" src="images/box-icon-glass.svg">
<h5 class="gray-text">Glass-Box</h5></a>
</li>
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<a class="nav-link pb-4" data-toggle="tab" href="#alternative">
<img class="box-icons pt-5 pb-4 mx-auto" src="images/box-icon-black.svg">
<h5 class="gray-text">Black-Box</h5></a>
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<p class="gray-text text-left text-lg-center">Glass-box models are interpretable due to their structure. Examples include: Explainable Boosting Machines (EBM), Linear models, and decision trees.<br><br>Glass-box models produce lossless explanations and are editable by domain experts.</p>
</ul>
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<ul class="list-group media-list media-list-stream">
<p class="gray-text text-left text-lg-center">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.</p>
</ul>
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</div>
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<h2 class="white">Wide Variety of Techniques</h2>
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<div class="col-12 col-lg-6 text-center my-auto">
<img class="global element-image mx-auto img-fluid" src="images/interpretml-global.png">
<img class="local element-image mx-auto img-fluid" src="images/interpretml-local.png">
<img class="group element-image mx-auto img-fluid" src="images/interpretml-group.png">
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<div class="technique-card tc-1 px-5 py-4 text-center text-lg-left white-card">
<h3 class="technique-text dark">Global</h3>
<p class="technique-text dark">Explore overall model behavior and find top features affecting model predictions using global feature importance</p>
</div>
</div>
<div class="col-12 col-lg-9 mx-auto">
<div class="technique-card tc-2 px-5 py-4 text-center text-lg-left">
<h3 class="technique-text white">Local</h3>
<p class="white technique-text">Explain an individual prediction and find features contributing to it using local feature importance</p>
</div>
</div>
<div class="col-12 col-lg-9 mx-auto">
<div class="technique-card tc-3 px-5 py-4 text-center text-lg-left">
<h3 class="technique-text white">Subset</h3>
<p class="white technique-text">Explain a subset of predictions using group feature importance</p>
</div>
</div>
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<div class="technique-card tc-4 px-5 py-4 text-center text-lg-left">
<h3 class="technique-text white">Feature Impact</h3>
<p class="white technique-text">See how changes to input features impact predictions with techniques like what-if analysis</p>
</div>
</div>
</div>
</div>
</div>
</div>
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<h2 class="pt-5 pb-2 dark">What You Can Do With InterpretML</h2>
</div>
</div>
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<div class="col-md-6">
<img class="img-fluid mx-auto ml-lg-0 interactive-visualization-icon" src="images/image-1.png" alt="">
</div>
<div class="col-md-5 align-self-center pr-lg-5 pb-4">
<h3 class="text-center text-lg-left pb-2 dark">Explore Your Data and Model Performance</h3>
<li class="gray-text img-fluid check-icon">Understand how model performance changes for different subsets of data and compare multiple models</li>
<li class="gray-text img-fluid check-icon">Explore model errors</li>
<li class="gray-text img-fluid check-icon">Analyze dataset statistics and distributions</li>
</div>
</div>
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<div class="col-md-5 align-self-center pl-lg-5 pb-4 order-2 order-md-1">
<h3 class="text-center text-lg-left pb-2 dark">Gain Model Understanding</h3>
<li class="img-fluid check-icon">Explore global and local explanations</li>
<li class="img-fluid check-icon">Filter data to observe global and local feature importance</li>
<li class="img-fluid check-icon">Run what-if analysis to see how model explanations change if you edit datapoints’ features</li>
</div>
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<img class="img-fluid mx-auto mr-lg-0 interactive-visualization-icon-2 pb-5" src="images/interactive-visualization-dashboard-icon.png" alt="">
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<h2 class="dark">Who Can Benefit from InterpretML?</h2>
</div>
</div>
<div class="row justify-content-lg-center">
<div class="col-12 col-lg-3 text-center text-lg-left">
<img class="img-fluid benefits-icons pb-4" src="images/icon-06.png">
<h3 class="dark pb-2">Data Scientists</h2>
<p class="gray-text">Understand models, debug or uncover issues and explain your model to other stakeholders.</p>
</div>
<div class="col-12 col-lg-3 text-center text-lg-left">
<img class="img-fluid benefits-icons pb-4" src="images/icon-07.png">
<h3 class="dark pb-2">Auditors</h2>
<p class="gray-text">Validate a model before it is deployed and audit it post-deployment.</p>
</div>
<div class="col-12 col-lg-3 text-center text-lg-left">
<img class="img-fluid benefits-icons pb-4" src="images/icon-08.png">
<h3 class="dark pb-2">Business Leaders</h2>
<p class="gray-text">Understand how models behave, in order to provide transparency about predictions to customers.</p>
</div>
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<img class="img-fluid benefits-icons pb-4" src="images/icon-09.png">
<h3 class="dark pb-2">Researchers</h2>
<p class="gray-text">Easily integrate with new interpretability techniques and compare against other algorithms.</p>
</div>
</div>
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<h1 class="pt-5 pb-4">Getting Started</h1>
<a class="models-behavior-btn" href="https://github.com/interpretml/interpret/">Install InterpretML</a>
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<h2 class="dark pt-4 pb-3">Contribute to InterpretML</h2>
<p class="gray-text">We encourage you to join the effort and contribute feedback, algorithms, ideas and more, so we can evolve the toolkit together!</p>
<a class="contribute-btn" href="https://github.com/interpretml/interpret/blob/master/CONTRIBUTING.md">Contribute</a>
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<h3 class="white">Resources</h3>
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<div class="col-12 col-lg-4 text-center text-lg-left pt-1 pb-4 pb-md-0">
<p class="white footer-italic">Learn More About InterpretML</p>
<a href="https://interpret.ml/docs">
<p class="white footer-bold">InterpretML documentation</p>
</a>
<a href="https://github.com/interpretml/interpret">
<p class="white footer-bold">Interpretability for Tabular Data</p>
</a>
<a href="https://github.com/interpretml/dice">
<p class="white footer-bold">Counterfactual Example Analysis</p>
</a>
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<p class="white footer-bold">Contact Us</p>
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<p class="white copyright-text">Copyright © 2023 The InterpretML Contributors</p>
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