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Tag: SHAP

  • Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

    Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more


    Price: $14.43
    (as of Dec 24,2024 08:11:05 UTC – Details)




    ASIN ‏ : ‎ B0B2PTF5PC
    Publisher ‏ : ‎ Packt Publishing; 1st edition (July 29, 2022)
    Publication date ‏ : ‎ July 29, 2022
    Language ‏ : ‎ English
    File size ‏ : ‎ 18121 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 304 pages


    In the world of machine learning, one of the biggest challenges that researchers and practitioners face is the lack of transparency and interpretability of models. This is especially important in practical applications where decisions made by machine learning models can have significant real-world consequences.

    One way to address this issue is through the use of explainability techniques, which aim to make machine learning models more interpretable and trustworthy. Some popular techniques for explainability include Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP).

    LIME is a technique that can explain the predictions of any machine learning model by approximating it with a simpler, more interpretable model that is locally faithful to the original model. This allows users to understand why a model made a particular prediction for a specific instance, making the model more transparent and trustworthy.

    On the other hand, SHAP is a unified approach to explain the output of any machine learning model. It assigns each feature an importance value for a particular prediction, providing a global view of how each feature contributes to the model’s output. This can help users understand the overall behavior of the model and identify potential biases or errors.

    By incorporating these explainability techniques into machine learning models, researchers and practitioners can make their models more transparent, interpretable, and trustworthy for practical applications. This not only helps build trust with stakeholders and end-users but also enables better decision-making and problem-solving in real-world scenarios.
    #Applied #Machine #Learning #Explainability #Techniques #models #explainable #trustworthy #practical #applications #LIME #SHAP

  • Interpreting Machine Learning Models With SHAP: A Guide With Python Examples And Theory On Shapley Values

    Interpreting Machine Learning Models With SHAP: A Guide With Python Examples And Theory On Shapley Values


    Price: $35.00
    (as of Dec 18,2024 00:31:47 UTC – Details)




    ASIN ‏ : ‎ B0CHL7W1DL
    Publisher ‏ : ‎ Independently published (September 7, 2023)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 208 pages
    ISBN-13 ‏ : ‎ 979-8857734445
    Item Weight ‏ : ‎ 1.07 pounds
    Dimensions ‏ : ‎ 7.44 x 0.47 x 9.69 inches


    Interpreting Machine Learning Models With SHAP: A Guide With Python Examples And Theory On Shapley Values

    Machine learning models have become increasingly complex and accurate, making it difficult to understand how they arrive at their predictions. SHAP (SHapley Additive exPlanations) is a powerful tool that helps us interpret the output of these models by attributing the prediction to individual features.

    In this post, we will delve into the theory behind SHAP and provide practical examples using Python to demonstrate how it can be used to interpret machine learning models. We will cover the concept of Shapley values, how they are calculated, and how they can be used to explain the contribution of each feature to the model’s prediction.

    By the end of this guide, you will have a solid understanding of SHAP and be able to apply it to your own machine learning models to gain insights into how they work and make more informed decisions.

    So, buckle up and get ready to dive into the fascinating world of SHAP and Shapley values!
    #Interpreting #Machine #Learning #Models #SHAP #Guide #Python #Examples #Theory #Shapley #Values