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1. • • To summarize, a whitebox view of a system model shows the internal workings of the system, while a blackbox view shows the system's external behavior without revealing the details of its internal workings.
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   What is the difference between a black box and a white box model?

   • On the other hand, white-box models have observable/understandable behaviors, features, and relationships between influencing variables and the output predictions (think: linear regressions and decision trees), but are often not as performant as black-box models (i.e, lower accuracy, but higher explainability).

   White Box vs Black Box Models: Finding the Balance - Dataiku

   blog.dataiku.com/white-box-vs-black-box-models-balancing-interpretability-and-accuracy

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   What is a black box machine learning model?

   • Black box machine learning models (Black Boxes), on the other hand, rank higher on innovation and accuracy, but lower on transparency and interpretability. Black Boxes produce output based on your input data set, but do not – and cannot – clarify how they came to those conclusions.

   White Box vs. Black Box Algorithms in Machine Learning - ActiveState

   www.activestate.com/blog/white-box-vs-black-box-algorithms-in-machine-learning/

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   What is a white box machine learning model?

   • A white box machine learning model (White Box) is one that allows humans to easily interpret how it was able to produce its output and draw its conclusions, thereby giving us insight into the algorithm’s inner workings. White boxes are transparent in terms of: Examples of such models include linear trees, decision trees, and regression trees.

   White Box vs. Black Box Algorithms in Machine Learning - ActiveState

   www.activestate.com/blog/white-box-vs-black-box-algorithms-in-machine-learning/

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   Can a black box model be explainable?

   • A white-box model is explainable by design. Therefore, it does not require additional capabilities to be explainable. A black-box model is not explainable by itself. Therefore, to make a black-box model explainable, we have to adopt several techniques to extract explanations from the inner logic or the outputs of the model.

   Black-box and White-Box Models towards Explainable AI

   towardsdatascience.com/black-box-and-white-box-models-towards-explainable-ai-172d45bfc512

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3. towardsdatascience.com › machine-learningBlack-box vs. white-box models. Most machine learning systems ...

   towardsdatascience.com › machine-learning

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   Mar 14, 2019 · They serve primarily as ‘global summary’ of a model. The following steps illustrate how you can build a surrogate model for any black-box model. Train a black-box model. Evaluate the black box model on the dataset. Choose an interpretable surrogate model (typically a linear model or a decision tree).

   • Author: Lars Hulstaert

4. www.activestate.com › blog › white-box-vs-black-boxWhite Box vs. Black Box Algorithms in Machine Learning

   www.activestate.com › blog › white-box-vs-black-box

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   Jul 19, 2023 · What Is a Black Box Machine Learning Model and How Do You Build One? Black box machine learning models (Black Boxes), on the other hand, rank higher on innovation and accuracy, but lower on transparency and interpretability.

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6. ieeexplore.ieee.org › document › 8882211Black-Box vs. White-Box: Understanding Their Advantages and ...

   ieeexplore.ieee.org › document › 8882211

   Oct 24, 2019 · This paper shows that both approaches are suitable for solving practical problems, but experts in machine learning need to understand the input data, the problem to solve, and the best way for showing the output data before applying a machine learning model.

   • Author: Octavio Loyola-González

   • Publish Year: 2019

7. blog.dataiku.com › white-box-vs-black-box-modelsWhite Box vs Black Box Models: Finding the Balance - Dataiku

   blog.dataiku.com › white-box-vs-black-box-models

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   Jan 31, 2020 · White Box vs Black Box Models: Finding the Balance. Data scientists and business leaders building or using machine learning models and AI systems face a serious challenge today — how to balance interpretability and accuracy stemming from the difference between black-box and white-box models.

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9. smitan94.github.io › Black-Box-vs-White-BoxWhite box vs Black Box models: Importance of interpretable ...

   smitan94.github.io › Black-Box-vs-White-Box

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   Sep 2, 2020 · White box vs Black Box models: Importance of interpretable model in today’s world - Smitan Pradhan. 5 minute read. INTRODUCTION: Machine Learning has come a long way in the last decade or so.

10. medium.com › @tam › comparing-black-box-vsComparing black-box vs. white-box modeling | by Tamanna - Medium

    medium.com › @tam › comparing-black-box-vs

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    Aug 4, 2021 · White-box models, on the other hand, like linear regressions and decision trees, have behavior, features, and relationships between influencing variables and output predictions, but are often...

11. towardsdatascience.com › black-box-and-white-boxBlack-box and White-Box Models towards Explainable AI

    towardsdatascience.com › black-box-and-white-box

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    Jun 23, 2021 · White Box vs. Black Box Models. An AI model can be (i) white-box or (ii) black-box. A white-box model is explainable by design. Therefore, it does not require additional capabilities to be explainable. A black-box model is not explainable by itself.