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  • Interpretable Machine Learning - Christoph Molnar
    On my free day, I explored topics that interested me, and interpretable machine learning eventually caught my focus Expecting plenty of resources on interpreting machine learning models, I was surprised to find only scattered research papers and blog posts, with no comprehensive guide
  • 2 Interpretability – Interpretable Machine Learning
    Using interpretable machine learning methods, you would find that the misclassification was due to the snow on the image The classifier learned to use snow as a feature for classifying images as “wolf,” which might make sense in terms of separating wolves from huskies in the training dataset but not in real-world use
  • 4 Methods Overview – Interpretable Machine Learning
    Figure 4 4: The big picture of (model-agnostic) interpretable machine learning The real world goes through many layers before it reaches the human in the form of explanations Separating the explanations from the machine learning model (= model-agnostic interpretation methods) has some advantages (Ribeiro, Singh, and Guestrin 2016)
  • 1 Introduction – Interpretable Machine Learning
    Interpretable Machine Learning, or Explainable AI, has really exploded as a field around 2015 (Molnar, Casalicchio, and Bischl 2020) Especially the subfield of model-agnostic interpretability, which offers methods that work for any model, gained a lot of attention
  • 18 SHAP – Interpretable Machine Learning - Christoph Molnar
    SHAP connects LIME and Shapley values This is very useful to better understand both methods It also helps to unify the field of interpretable machine learning SHAP has a fast implementation for tree-based models I believe this was key to the popularity of SHAP because the biggest barrier for adoption of Shapley values is the slow computation
  • 3 Goals of Interpretability – Interpretable Machine Learning
    Interpretable machine learning is useful not only for learning about the data, but also for learning about the model For example, if you want to learn about how convolutional neural networks work, you can use interpretability to study what concepts individual neurons react to
  • 17 Shapley Values – Interpretable Machine Learning
    Looking for a comprehensive, hands-on guide to SHAP and Shapley values? Interpreting Machine Learning Models with SHAP has you covered With practical Python examples using the shap package, you’ll learn how to explain models ranging from simple to complex It dives deep into the mechanics of SHAP, provides interpretation templates, and highlights key limitations, giving you the insights you
  • 21 Feature Interaction – Interpretable Machine Learning
    When features interact with each other in a prediction model, the prediction cannot be expressed as the sum of the feature effects because the effect of one feature depends on the value of the other feature Aristotle’s predicate “The whole is greater than the sum of its parts” applies in the presence of interactions What are feature interactions? If a machine learning model makes a
  • 12 Ceteris Paribus Plots – Interpretable Machine Learning
    Ceteris paribus (CP) plots (Kuźba, Baranowska, and Biecek 2019) visualize how changes in a single feature change the prediction of a data point Ceteris paribus plots are one of the simplest analysis one can do, despite the complex-sounding Latin name, which stands for “other things equal” and means changing one feature but keeping the others untouched 1 It’s so simple since it only
  • 14 LIME – Interpretable Machine Learning - Christoph Molnar
    Local surrogate models are interpretable models that are used to explain individual predictions of black box machine learning models Local interpretable model-agnostic explanations (LIME), proposed by Ribeiro, Singh, and Guestrin (2016), is an approach for fitting surrogate models Surrogate models are trained to approximate the predictions of the underlying black box model The idea is quite





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