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<h1 class="post-title entry-title">Shap interaction values.
May 23, 2025 · This is where SHAP comes in. </h1>
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Shap interaction values y numpy. SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. Shapley Interaction Quantification (shapiq) is a Python package for (1) approximating any-order Shapley interactions, (2) benchmarking game-theoretical algorithms for machine learning, (3) explaining feature interactions of model predictions. sum(axis= 1) # 根据shap值=主效应 + 交互效应 计算shap值 if np. DataFrame or catboost. 背景. expected_value at the bottom of the plot. May 23, 2025 · This is where SHAP comes in. We then dive into 3 different aggregations of these values Nov 14, 2024 · To detect and visualize feature interactions, we will use SHAP dependence plots, which show the relationship between a feature and the SHAP value, and how the feature’s value interacts with another feature. Jun 28, 2023 · SHAP (SHapley Additive exPlanations) values are a way to explain the output of any machine learning model. array. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations). See how the SHAP values and the SHAP interaction values change when adding an interaction term to a tree-based model. shap交互效应旨在剔除特征的主效应，专注于分析模型中特征之间的交互对shap值的影响。具体而言，交互效应值反映了一个特征对预测的贡献如何随着另一个特征的变化而变化。 Nov 28, 2024 · x1_shap_values = shap_values[:, 0] # 提取x_1的shap值 x1_main_effect = shap_interaction_values[:, 0, 0] # 提取x_1的主效应 x1_shap_total = x1_main_effect + df_1. See full list on towardsdatascience. At present this is using a grid of SHAP dependency plots. They provide a fair, consistent, and mathematically sound way to break down any prediction into individual feature contributions. Notice that the lines do not completely converge to explainer. We calculate the SHAP values (base, main effect and feature interactions) of this fitted model and show the most useful way (that we have found) to present all of these values in order to gain the most insight into how the model is working. Pool (for catboost) A matrix of samples (# samples x # features) on which to explain the model’s output. In machine learning, each feature is assigned an importance value representing its contribution to the model's output. com That’s where SHAP (SHapley Additive exPlanations) values come in. sum(axis = 1)): print("验证成功：X_1 的总 SHAP SHAP interaction values The decision plot supports SHAP interaction values as shown here. Dec 4, 2021 · Specifically, we start by explaining what SHAP interaction values are and how they can be used to explain individual predictions. Learn how to compute and interpret SHAP interaction values for a simple linear function with an interaction term. What Are SHAP Values? SHAP (SHapley Additive exPlanations) is a unified framework for interpreting machine learning models. This separation can uncover Nov 28, 2024 · 背景. Parameters: X numpy. I remember the first time I used SHAP on a real-world project—it was like turning on the lights in a dark room. In this post, we’ll explore visualizing SHAP values for model explainability, why it matters, how SHAP works, and how to implement SHAP visualizations to gain meaningful insights. This is because there are N(N + 1)/2 = 12(13)/2 = 78 features including interaction and main effects, but the decision plot shows only the 20 most Nov 14, 2024 · To detect and visualize feature interactions, we will use SHAP dependence plots, which show the relationship between a feature and the SHAP value, and how the feature’s value interacts with Jan 8, 2022 · 文章浏览阅读3. 3w次，点赞55次，收藏240次。SHAP的理解与应用 SHAP有两个核心，分别是shap values和shap interaction values，在官方的应用中，主要有三种，分别是force plot、summary plot和dependence plot，这三种应用都是对shap values和shap interaction values进行处理后得到的。 An interaction may speak more than a thousand main effects. # The interaction_index argument can be used to explicitly # set which feature gets used for coloring shap. 本文提出了一种独特的shap可视化方法，与传统的shap值依赖图有所不同。传统的shap值依赖图通常以单个特征的总shap值（即该特征的主效应与所有交互效应的综合贡献）作为y轴，而本文的方法将y轴替换为单个特征的 shap主效应值 ，即该特征在独立作用时对模型预测的贡献，这种方式剥离了交互 . array, pandas. shap_interaction_values (X, y = None, tree_limit = None) Estimate the SHAP interaction values for a set of samples. An array of label values for each sample. It uses a game theoretic approach that measures each player's contribution to the final outcome. 使用SHAP Python包识别和可视化数据中的交互可直接在橱窗里购买，或者到文末领取优惠后购买： SHAP 值用于解释模型做出的个别预测。它通过给出每个因素对最终预测的贡献来实现这一点。SHAP 交互值在此基础上进行了… Jan 17, 2020 · SHAP interaction values have properties similar to SHAP values 13, and allow the separate consideration of interaction effects for individual model predictions. allclose(shap_values [:,0], x1_main_effect + df_1. dependence_plot ("rank(1)", shap_values, X, interaction_index = "Education-Num") Exploring different interaction colorings ¶ Welcome to the SHAP documentation . </span></span></span><span class="date meta-item tie-icon"></span></div>
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