Shap interaction values 954230 Pclass 0. set_param ({"device": "cuda"}) shap_values = model. interaction value是将SHAP值推广到更高阶交互的一种方法。树模型实现了快速、精确的两两交互计算，这将为每个预测返回一个矩阵，其中主要影响在对角线上，交互影响在对角线外。 shap_interaction_values = explainer. SHAP Interaction Values: These values show how much the interaction between two features contributes to the prediction. Not only can SHAP tell you which features are important, but it can also explain how features interact with each other Oct 1, 2021 · (c) SHAP reveals heterogeneity and interactions. Aug 6, 2022 · 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。 代码实现 a waterfall plot Oct 2, 2023 · SHAP interaction values distribute model scores among all feature main effects and pairwise interactions [3]. While SHAP dependence plots are the best way to visualize individual interactions, a decision plot can display the cumulative effect of main effects and interactions for one or more observations. Feb 25, 2021 · In the example above, we have two relationships plotted: priors_count and sex with both showing the interactions with age. 3 with shap 0. iloc[:,:]) I receive this error: AttributeError: 'Kernel' object has no attribute 'shap_interaction_values' 这些数值往往揭示了有趣的隐藏关系(交互作用) shap_interaction_values = explainer. the calculated interaction_values are Nan or 0. In the present paper, we adopted the Shapley additive explanation (SHAP), which is based on fair profit allocation among many stakeholders depending on their contribution, for interpreting a gradient-boosting decision tree model using hospital data. The link function used to map between the output units of the model and the SHAP value units. summary_plot(shap_interaction_values, X) 所有特征之间的交互 对角线上是该特征自身的主要影响 Jul 23, 2021 · In short, SHAP values represent a feature's responsibility for a change in the model output. SHAP Interaction In the first form we know the values of the features in S because we observe them. 2 SHAP Interaction Value Dependence Plots 不清楚这个SHAP Interaction Value具体是怎么算的，不过这里的图和4. interaction value是将SHAP值推广到更高阶交互的一种方法。树模型实现了快速、精确的两两交互计算，这将为每个预测返回一个矩阵，其中主要影响在对角线上，交互影响在对角线外。 # ensure the main effects from the SHAP interaction values match those from a linear model. 2 and 2. 4 特征组合的影响 将交互图按特征重要性排序后绘图．个人认为下图非常有用，它将单特征与特征组合画在一张图中，可以从中分析出哪些特征组合更为重要． Feb 11, 2024 · Photo by Suhash Villuri on Unsplash. Partial dependence plots display SHAP values against a specific feature, and color the observations according to another feature. Fast exact computation of pairwise interactions are implemented for tree models with shap. 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 这里还有一个使用经验，如果只是用shap interaction values绘制归因关系的话，一开始explainer. Mar 8, 2025 · shap. TreeExplainer (model). 6w次，点赞14次，收藏124次。突然发现这篇文章居然被百度文库给盗了, 举报侵权还要我自己打印保证函, 最逗的是, 上传保证函图片还要求开启flash,其心昭然若揭. 121324 0. Setting Up SHAP in R (Hands-on Guide) “Tell me and I forget, teach me and I remember, involve me and I learn. Apr 3, 2024 · Third observation prediction explanation: the x0 and x1 values are pushing the prediction value lower and the x2 value is slightly nudging the value lower. A "ggplot" (or "patchwork") object, or - if kind = "no" - a named numeric matrix of average absolute SHAP interactions sorted by the average absolute SHAP values (or a list of such matrices in case of "mshapviz" object). This process ensures that SHAP values follow three essential properties: Efficiency: The sum of all SHAP values, for instance, shows the combined effect of its features on the model’s prediction DMatrix (X, label = y, feature_names = data. On the x-axis is the SHAP value. output_rank_order “max”, “min”, “max_abs”, or “custom” Sep 19, 2024 · shap. SHAP 决策图显示复杂模型如何得出其预测（即模型如何做出决策）。决策图是 SHAP value 的文字表示，使其易于解读。 shap_interaction_values (X, y = None, tree_limit = None) Estimate the SHAP interaction values for a set of samples. A simple worked example of Shap; Examining interactions between features with SHAP interactions: Using Titanic survival as an example SHAP values are calculated SHAP interaction values are a generalization of SHAP values to higher order interactions. dependence_plot("Subscription Length", shap_values[0], X_test,interaction_index="Age") A dependence plot is a type of scatter plot that displays how a model's predictions are affected by a specific feature (Subscription Length). 4. 보스턴 주택 데이터셋을 활용해보겠습니다. expected_value, shap_values, X_test) Think of this as a waterfall chart for each prediction—showing how features push the model’s output higher or lower. As we think the package can leave the early development stage, we want to introduce our work to a broader audience. In general, the second form is usually preferable, both because it tells us how the model would behave if we were to intervene and change its inputs, and also because it is much easier to compute. summary_plot(shap_interaction_values, train_X) SHAP Interaction Values SHAP interaction values are a generalization of SHAP values to higher order interactions. sum(axis= 1) # 根据shap值=主效应 + 交互效应 计算shap值 if np. Our journey through the intricacies of interpretable machine learning continues! In this episode, we’ll venture beyond simplistic interpretations and delve into the practicalities and pitfalls of interpreting models like GBMs with non-normal losses. links. 5 it returns the Aug 21, 2022 · shap_interaction_values = explainer. Jul 26, 2024 · 4. 8 SHAP 相互作用値 (SHAP Interaction Values) 相互作用効果は、個々の特徴量の影響を考慮した後の追加の複合的な特徴量の効果です。 ゲーム理論から、シャープレイ相互作用は下記のように定義できます。 Feb 11, 2024 · Photo by Suhash Villuri on Unsplash. interaction plot shap_interaction_values = shap. This returns a matrix for every prediction, where the main effects are on the diagonal and May 13, 2024 · 接下来，通过切片操作从 shap_values 中提取出每个类别的 SHAP 值，分别存储shap_values_class_1，shap_values_class_2 和 shap_values_class_3 中。。为后续的工作准备好所需的工具，我们需要引入如 numpy 、pandas 用于数据处理，xgboost 用于模型构建，用于模型解释的shap，用于可视化的seaborn和matplotlib，以及 sklearn 中的 Sep 10, 2023 · shap_interaction_values = explainer. This sequential ordering formulation also allows for easy reuse of model evaluations and the ability to efficiently avoid evaluating the model when the background values for a feature are the same as the Oct 28, 2024 · The vertical spread of SHAP values at a fixed feature value is a sign of interaction effects with other features in the model. Conclusions. This is not the fastest method, however, it is done this way Jun 5, 2020 · Image source: SHAP github Vocabulary. Methods (by class) sv_interaction(default): Default method. 2. SHAP : SHapley Additive exPlanations. It is important to consider the inset histogram when interpreting the trends. summary_plot(shap_interaction_values, X) 4. shap_interaction_values(X). abs(shap_interaction_values). ” — Benjamin Franklin. com Jun 28, 2023 · shap. shape [1]) total = 0 for i in Jan 7, 2025 · Interaction Values: SHAP values can capture interactions between features. Meant to approximate SHAP values for deep learning models. 3w次，点赞55次，收藏239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。 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. shape [1]) total = 0 for i in # The interaction_index argument can be used to explicitly # set which feature gets used for coloring shap. Learn how to compute and interpret SHAP interaction values for a simple linear function with an interaction term. 10. SHAP values for feature i when feature j is present and B. In case there is no built-in function available, is there a rigorous method to compute SHAP interaction values starting from SHAP values? Any reference or suggested approach would be greatly appreciated. shap interaction values则是特征俩俩之间的交互归因值，用于捕捉成对的相互作用效果，由于shap interaction values得到的是相互作用的交互归因值，假设有N个样本M个特征时，shap values的维度 Jan 30, 2023 · 文章浏览阅读3. To our knowledge, SHAP interaction values have not yet been applied to analyze financial data set. 458359 Age -0. Jul 23, 2024 · Discover interactions: SHAP values can expose unforeseen feature interactions, promoting the generation of new, performance-enhancing features. KernelExplainer(mlpreg2. diag_indices (shap_interaction_values. Gradient color indicates the original value for that variable. but shap_values is running fine. 文章可解释性机器学习_Feature Importance、Permutation Importance、SHAP 来看一下SHAP模型，是比较全能的模型可解释性的方法，既可作用于之前的全局解释，也可以局部解释，即单个样本来看，模型给出的预测值和某些特征可能的关系，这就可以用到SHAP。 Jul 18, 2019 · Quote paper 2: “SHAP interaction values can be interpreted as the difference between the SHAP values for feature i when feature j is present and the SHAP values for feature i when feature j is absent. SHAP interaction effect between the i-th and j-th is split equally (i. Each row sums to the difference between the model output for that sample and the expected value of the model output (which is stored as the expected_value attribute of the explainer). ϕᵢⱼ=ϕⱼᵢ) and Nov 28, 2024 · 背景. When I use shap for xgboost , the question 2 also is existed. Oct 1, 2021 · (c) SHAP reveals heterogeneity and interactions. hclust, which orders the samples based on a hierarchical clustering by their explanation similarity. Our formulation of interventional SHAP algorithms also applies to interaction values resulting in more efcient algorithms for computing SHAP interaction values for tree-based models. Nov 28, 2024 · 背景. This tells us whether the model has learned any synergy or redundancy between features. 知乎-SHAP知识点全汇总 shap. Il se base sur un exemple de classification de données tabulaires. 이러한 특성 상호작용을 강조하는 것으로 dependence plot은 개선될 수 있다. This returns a matrix for every prediction, where the main effects are on the diagonal and the interaction effects Feb 12, 2018 · We then extend SHAP values to interaction effects and define SHAP interaction values. Similarly, the sensitivities of a positive discrepancy based on SHAP values (S-OR W > S-OR M) were 86, 99, and 100%, respectively. 28. A keen observer will note the following: Priors count has a positive relationship with its associated SHAP value - more priors indicates a larger positive impact on the likelihood of recidivism. train (param, dtrain, num_round) # Compute shap values using GPU with xgboost model. Parameters: X numpy. It is important to understand all the bricks that make up a SHAP explanation. dependence_plot("cholesterol", shap_values[1], X_test, interaction_index="age") This code will generate a plot showing the interaction between cholesterol levels and age, providing you with a Passing a matrix of SHAP values to the heatmap plot function creates a plot with the instances on the x-axis, the model inputs on the y-axis, and the SHAP values encoded on a color scale. logit can be useful so that expectations are computed in probability units while explanations remain in the (more naturally additive) log-odds units. DeepExplainer class shap. # Visualize feature interactions with SHAP Sep 24, 2023 · # ensure the main effects from the SHAP interaction values match those from a linear model. This returns a matrix for every prediction, where the main effects are on the diagonal and the interaction effects We can iterate this many times over many random permutations to get better SHAP value estimates for models with higher order interactions. , 2022). An array of label values for each sample. 4 特征组合的影响 将交互图按特征重要性排序后绘图．个人认为下图非常有用，它将单特征与特征组合画在一张图中，可以从中分析出哪些特征组合更为重要． Aug 15, 2021 · I want to calculate the shap_interaction values. SHAP values can also help interpret interaction effects between features. This May 20, 2022 · 文章浏览阅读1. # ensure the main effects from the SHAP interaction values match those from a linear model. Mar 7, 2019 · I got following error while running shap_interaction_values. kmeans(trainX, 10) explainer = shap. plots. For one observation, interactions for a feature pair ( i and j ), are calculated by measuring the Shapley value for a feature i given its original value of j . the SHAP values for feature i when feature j is absent. the calculated interaction_values are Nan or 0. 我们也可以多个变量的交互作用进行分析。一种方式是采用 summary_plot 描绘出散点图. iloc [: 2000,:]) Sep 16, 2020 · WHen I use shap_interaction_values for catboost, some problem: 'TreeEnsemble' object has no attribute 'values'. This is an enhanced version of the DeepLIFT algorithm (Deep SHAP) where, similar to Kernel SHAP, we approximate the conditional expectations of SHAP values using a selection of background samples. The key idea of SHAP is to calculate the Shapley values for each feature of a sample to be interpreted, where each Shapley value represents the impact that the feature to which it is associated generates in the Feb 1, 2022 · When using machine learning techniques in decision-making processes, the interpretability of the models is important. One way to do this is to use a SHAP partial dependence plot (Figure 9). 특히 상호작용의 경우 SHAP dependence plot은 Y축에서 훨씬 더 분산된다. Jun 6, 2022 · Hi, I'm explaining models loaded on python from weka and I was trying to use the summary_plot with the shap_interaction_values, but when i try to do it: shap_interaction_values = explainer. shap_interaction_value Mar 9, 2025 · 3. identity, but shap. shap_values(X_test_scaled) Step 2: Visualize Feature Interactions. Fast exact computation of pairwise interactions are implemented in the later versions of XGBoost (>=1. shap_interaction_values(X)返回shap interaction values时不需要使用全量的X，对X进行抽样即可，毕竟shap interaction values的计算时间还是较长的，而且最后绘制归因关系的散点图时也不 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… Oct 11, 2021 · shap_interaction_values = explainer. Features pushing the prediction higher are distinguished from those pushing the prediction lower. scatter (interaction_shap_values [:,:, 0]) Have an idea for more helpful examples? Pull requests that add to this documentation notebook are encouraged! 5. In the second form we know the values of the features in S because we set them. 5w次，点赞106次，收藏821次。**SHAP是Python开发的一个“模型解释”包，可以解释任何机器学习模型的输出**。其名称来源于**SHapley Additive exPlanation**，在合作博弈论的启发下SHAP构建一个加性的解释模型，所有的特征都视为“贡献者”。 Jun 4, 2020 · Cet article est un guide des fonctionnalités avancées et moins connues de la librairie python SHAP. Jul 20, 2019 · Hey @slundberg, Thanks again for this great package!According to your paper Consistent Individualized Feature Attribution for Tree Ensembles, it says "SHAP interaction values can be interpreted as the difference between the A. order. Sep 20, 2024 · SHAP interaction values are like that hidden door you didn’t see coming. If shap_values contains interaction values, the number of features is automatically expanded to include all possible interactions: N(N + 1)/2 where N = shap_values. Here, the features' main effects are seen in the main diagonal, while the interaction effects with other features are shown Aug 20, 2022 · shap_interaction_values = explainer. Nov 14, 2024 · shap_values = explainer. Here the SHAP values for the main effects are given on 背景. " shap_interaction_values (X, y = None, tree_limit = None) Estimate the SHAP interaction values for a set of samples. 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 Dec 24, 2019 · 그 이유는 PDP와 ALE은 평균적인 효과를 나타내지만 SHAP dependence은 Y축에 대한 분산도 보여주기 때문이다. Interpreting Interaction Effects. Feb 3, 2022 · Interaction Values. shapviz offers a heuristic to pick another feature on the color scale with potential strongest interaction. force_plot(explainer. model_selection import train_test_split import shap X, y = shap. Dec 7, 2021 · shap_interaction_values = explainer. Compute SHAP Interaction Values See the Tree SHAP paper for more details, but briefly, SHAP interaction values are a generalization of SHAP values to higher order interactions. May 15, 2024 · 一、关于 SHAP. Note that SHAP interaction values are multiplied by two (except main effects). 121324 -0. 6 SHAP交互值 Nov 19, 2024 · SHAP values, based on game theory, quantify the contribution of each feature to a model’s prediction. dependence_plot ("rank(1)", shap_values, X, interaction_index = "Education-Num") Exploring different interaction colorings ¶ Apr 26, 2019 · 次に、Interaction SHAPを計算してみます。 shap_interaction_values = explainer. Interaction Values. 安装使用示例shap_values()KernelExplainer返回值使用KernelExplainer可视化SHAP医学解释相关论文项目实践堆叠热力图汇总SHAP值_python shap包 Estimated SHAP values, usually of shape (# samples x # features). The primary advantage of the SHAP values lies in their ability to reflect the impact of features on each sample, illustrating both positive and Jan 29, 2023 · Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand Feb 18, 2021 · Or does it give a measure of feature-feature interactions in the direction of larger shap values and positive predictions specifically? Here is the heatmap I am trying to understand from the link: I guess what I am asking is what np. Using geom_sina from ggforce to make the sina plot; We can see clearly for the most influential variable on the top: Monthly water cost. 0. SHAP（SHapley Additive exPlanations） 是一种博弈论方法，用于解释任何机器学习模型的输出。 它将最优信用分配与局部解释联系起来，使用博弈论中的经典 Shapley 值及其相关扩展（有关详细信息和引文，请参阅论文）。. to analyze financial data set. Thus, SHAP values streamline the feature engineering process, amplifying the model’s predictive prowess by facilitating the extraction of the most pertinent features. force_plot * For each feature, the sum of the SHAP main effect and all of its SHAP interaction values = SHAP value for the feature (shown in "Total", and can be compared to the SHAP values above) male Pclass Age SibSp Total male -1. shape [1]) total = 0 for i in Jul 18, 2019 · Quote paper 2: “SHAP interaction values can be interpreted as the difference between the SHAP values for feature i when feature j is present and the SHAP values for feature i when feature j is absent. See how the SHAP values and the SHAP interaction values change when adding an interaction term to a tree-based model. We propose a rich visualization of individualized feature attributions that improves over classic attribution summaries and partial dependence plots, and a unique "supervised" clustering (clustering based on feature attributions). 0) with the pred_interactions flag. data_int: the 3-dimention SHAP interaction values array. summary_plot (shap_interaction_values, X_train) Feature 사이의 interaction 또한 파악할 수 있습니다. SHAP依赖图可以替代部分依赖图（Partial Dependence Plot）和累积局部效应图（Accumulated Local Effects Plot）。SHAP依赖图也显示y轴上的方差，这是因为有其他特征的相互作用，所以依赖图在y轴上会分散。通过显示这些特性交互，可以改进依赖图。 3. Indicates how much is the change in log-odds. D’abord, parlons de la… Feb 23, 2025 · This code demonstrates how to handle categorical features by encoding them before training the model and calculating SHAP values. Jun 10, 2022 · A scatterplot of SHAP values of a feature like color against its observed values gives a great impression on the feature effect on the response. 2. This is particularly useful for understanding complex models like XGBoost. Several studies ha ve shown that gold prices may be affected . summary_plot(shap_interaction_values, X) dependence_plot 为了理解单个feature如何影响模型的输出，我们可以将该feature的SHAP值与数据集中所有样本的feature值进行比较。 Figure 4 shows the pairwise SHAP interaction values across all features. expected_value , shap_values [ 0 ,:], X . if data_int is supplied, y-axis will plot the interaction values of y (vs. sum(0) is specifically giving. 8 SHAP 相互作用値 (SHAP Interaction Values) 相互作用効果は、個々の特徴量の影響を考慮した後の追加の複合的な特徴量の効果です。 ゲーム理論から、シャープレイ相互作用は下記のように定義できます。 # takes a couple minutes since SHAP interaction values take a factor of 2 * # features # more time than SHAP values to compute, since this is just an example we only explain # the first 2,000 people in order to run quicker shap_interaction_values = 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 Apr 17, 2025 · SHAP Interaction Values. 4. SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. There are two ways we might want to compute SHAP values, either the full conditional SHAP values or the interventional SHAP values. See full list on towardsdatascience. 828846 0. Welcome to the SHAP documentation . 2017の論文ではSHAPを計算するときにfeature independenceを仮定していたと思うのだけど，今回のTree SHAPではそこはモデルの側に任されているので特に問題ではないよう． 2つの特徴量について一方だけ加えたときの変化量と，もう一方を加えて Feb 1, 2025 · In effect, using the SHAP analysis, each predictive sample represents a predictive value, and the SHAP value is the value derived for each characteristic in the predictive sample (Wang et al. e. x). If SHAP interaction values have been computed (via {xgboost} or {treeshap}), we can study them by sv_dependence() and sv_interaction(). shap_interaction_values(X)[0] We will now define some zero-matrices to fill with our calculations. predict, X_train_summary) shap_int_values = explainer. array, pandas. 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. iloc[0, :] is the corresponding feature values for the first observation shap. 5 it returns the Jan 10, 2022 · shap. 101960 0. I tried with both lgb 2. 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. Vertical scatter gives additional info on interaction effects. Aug 19, 2022 · 1 介绍. highlight Any Specify which observations to draw in a different line style. If "auto", will select the feature "c" minimizing the variance of the shap value given x and c, which can be viewed as a heuristic for the strongest interaction. Plotting this with my data gives same feature 由于复制粘贴会损失图片dpi请移步公众号原文观看获得更好的观感效果（关注公众号获得更多文章） SHAP全解析：机器学习、深度学习模型解释保姆级教程 什么是SHAP解释？在机器学习和深度学习领域，模型解释性是一个… Oct 1, 2023 · SHAP is a method introduced by Lundberg and Lee in 2017 for the interpretation of predictions of ML models through Shapely values. DataFrame or catboost. shap_interaction_values (X. By default the samples are ordered using shap. values # Force plot for the first observation with matplotlib # The expected_value is the model's expected output for the dataset # The shap_values[0] represents the SHAP values for the first observation # X_test. feature_names) model = xgb. SHAP interaction values are a generalization of SHAP values to higher order interactions. values, X, interaction_index = "HouseAge") To get an overview of which features are most important for a model we can plot the SHAP values of every feature for every sample. From this number we can extract the probability of success. 各特徴量が予測にどう影響する… Mar 30, 2020 · This allows us to use the algorithm for computing SHAP values to compute SHAP interaction values. Jan 28, 2021 · Recently we added an option to calculate SHAP Interaction Values. 'TreeEnsemble' object has no attribute 'values'. 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. If you’re like me, you don’t just want to read about SHAP—you want to see it in action. array. Apr 24, 2025 · Explanation): shap_values = shap_values. This separation can uncover Decision plots support SHAP interaction values: the first-order interactions estimated from tree-based models. 在这篇文章中，我们将介绍如何利用XGBoost模型进行多分类任务，并使用SHAP对模型进行解释，并生成SHAP解释图、依赖图、力图和热图，从而直观地理解模型的决策过程和特征的重要性 Sep 5, 2022 · SHAP 值 (SHapley Additive exPlanations的首字母缩写)对预测进行分解，以显示每个特征的影响。你可以在哪里使用这个?一个模型说，银行不应该借钱给某人，法律要求银行解释每笔拒绝贷款的依据医疗保健提供者想要确定是什么因素导致每个病人患某种疾病的风险，这样他们就可以通过有针对性的健康干预 Jan 6, 2025 · 目标 在这篇文章中，我们将介绍如何利用XGBoost模型进行多分类任务，并使用SHAP对模型进行解释，并生成SHAP解释图、依赖图、力图和热图，从而直观地理解模型的决策过程和特征的重要性 二分类模型和多分类模型在SHAP上的差异 二分类模型 Sep 16, 2020 · WHen I use shap_interaction_values for catboost, some problem: 1. 149247 0. datasets. 按照SHAP值由大到小排序，可以发现只考虑特征的主要影响时，与前文中特征排序的结果不一致； 参考：GitHub-Summary plot of SHAP interaction values ordered by feature importance; 分享到此结束，如有错误欢迎留言指正～求关注，求点赞～ 参考. DeepExplainer (model, data, session = None, learning_phase_flags = None) . predict (dtrain, pred_contribs = True) # Compute shap interaction values using GPU shap_interaction_values = model. shap_interaction_values(train_X) summary_plot で、各特徴量軸のペアについてのSHAPを確認することができます。 shap. allclose(shap_values [:,0], x1_main_effect + df_1. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. The SHAP interaction values show how the contribution of one feature depends on the value of another feature. 219196 -0. [5]: shap . 1. They decompose a model’s output into a baseline value (average prediction) and the sum of Aug 1, 2024 · 目标. summary_plot(shap_interaction_values, X) dependence_plot 为了理解单个feature如何影响模型的输出，我们可以将该feature的SHAP值与数据集中所有样本的feature值进行比较。 Feb 24, 2025 · Concretely, SHAP interaction values produce a matrix of attributions: the diagonal elements are the usual Shapley values for each feature (solo effect), and each off-diagonal element is the attribution for the interaction between feature i and feature j. Often, by using default values for parameters, the complexity of the choices we shap_interaction_values (X, y = None, tree_limit = None) Estimate the SHAP interaction values for a set of samples. I used the following script: X_train_summary = shap. shape[1]. In a realistic simulation study, the ability of the SHAP values to detect an interaction effect was proportional to its magnitude. What are SHAP values? SHAP stands for Shapley Additive Explanations — a method to explain model predictions based on Shapley Values from game theory. This returns a matrix for every prediction, where the main effects are on the diagonal and the interaction effects An interaction may speak more than a thousand main effects. Eventually, we present an approach for aggregating background data for interventional SHAP computation, strongly mitigating the impact of the background data on Apr 4, 2025 · Value. 3的区别主要在于，这里没有Vertical dispersion了，也就是通过某种方式把交互作用去掉了，只反映我想看的这个因素的影响。 3. shap_interaction_values(X. expected_value at the bottom of the plot. predict (dtrain Note that this causes a pair of values to be returned (shap_values, indexes), where shap_values is a list of numpy arrays for each of the output ranks, and indexes is a matrix that tells for each sample which output indexes were chosen as “top”. dependence_plot ("MedInc", shap_values. Sep 27, 2018 · I wanted to ask if there are any updates regarding the implementation of SHAP interaction values for DeepExplainer. Mar 18, 2019 · The y-axis indicates the variable name, in order of importance from top to bottom. In particular, as the main contribution of this paper, we provide a more efficient approach of interventional SHAP for tree-based models by precomputing statistics of the background data based on the tree structure. They're a powerful tool for understanding feature interactions. Oct 29, 2023 · 文章浏览阅读1w次，点赞17次，收藏66次。本文介绍了如何使用shap值来增强对机器学习模型的理解，包括排列重要性、部分依赖图和shap依赖性贡献图的应用，展示了如何通过这些可视化工具分析特征对预测的影响，特别是在足球数据和医院再入院预测中的应用。 Feb 17, 2019 · SHAP interaction values. force_plot ( explainer . shap_interaction_values(X) shap. Oct 14, 2018 · Summary plot. The value next to them is the mean SHAP value. どの特徴量が重要か: モデルが重要視している要因がわかるfeature importance2. ” The SHAP interaction values take time since it calculates all the combinations. y numpy. 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). boston() X_train, X SHAP Interactions. A Higher cost is associated with the declined share of temporary housing. summary_plot(shap_interaction_values, X) Decision plot. For interventional SHAP values we break any dependence structure between features in the model and so uncover how the model would behave if we intervened and changed some of the inputs. 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。 Jan 8, 2022 · 文章浏览阅读3. Jul 30, 2021 · 이번 시간엔 파이썬 라이브러리로 구현된 SHAP을 직접 써보며 그 결과를 이해해보겠습니다. Notice that the lines do not completely converge to explainer. The output can be seen in Figure 5. The type and shape of the return value depends on the number of model inputs and outputs: shap. 3️⃣ Dependence Plot (Feature Interactions) shap. shap_interaction_values (X_train) # main effect on the diagonal # interact effect off the diagonal shap. import pandas as pd import numpy as np # xgb 모델 사용 from xgboost import XGBRegressor, plot_importance from sklearn. dependence_plot("median_income", shap_values, X_test) Dec 4, 2021 · With the SHAP interaction values, we can extend on this plot by using the summary plot in the code below. TreeExplainer(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. # while the main effects no longer match the SHAP values when interactions are present, they do match # the main effects on the diagonal of the SHAP interaction value matrix dinds = np. dependence_plot (" MolLogP ", shap_values, df_test_X, interaction_index = 10) これはある説明変数の予測値への影響が、他の説明変数に依存しているかを確認することができる。 Dec 23, 2024 · TL;DR 機械学習のモデル解釈手法SHAPを拡張した、SHAP-IQ (Interaction Quantification) が提案された 単一の特徴量だけでなく、複数の特徴量間の交互作用も近似して、モデルの解釈性を深めることが可能 pythonライブラリshapiqで利用可能。NeurIPS2024 (Datasets and Benchmarks Track) に採択 SHAPと同様に、ローカル # create a SHAP dependence plot to show the effect of a single feature across the whole dataset shap. This returns a matrix for every prediction, where the main effects are on the diagonal and Abstract: 機械学習モデルと結果を解釈するための手法1. Jul 23, 2021 · To the best of our know ledge, SHAP interaction values hav e not yet been applied . Our algorithm can also be readily applied to computing SHAP interaction values for these models. 3的区别主要在于，这里没有Vertical dispersion了，也就是通过某种方式把交互作用去掉了，只反映我想看的这个因素的影响。 If shap_values contains interaction values, the number of features is automatically expanded to include all possible interactions: N(N + 1)/2 where N = shap_values. 077782 -0. 029967 -0. iloc [ 0 ,:]) May 16, 2023 · The core concept behind SHAP values is to allocate a specific value to each input feature, representing its contribution to a particular prediction. Pool (for catboost) A matrix of samples (# samples x # features) on which to explain the model’s output. We can use SHAP values to further understand the sources of heterogeneity. By default it is shap. Sep 26, 2021 · （4）对多个变量的交互进行分析. nxgquw rjjlz urvwp cgn ciy whpvm qoezyu zgrp kswk rnrw