Shap explainer SHAP is based on the Shapley value - originating from the game theory. Explainer(model. These explainers enable attribution of output predictions to input f A detailed guide to use Python library SHAP to generate Shapley values (shap values) that can be used to interpret/explain predictions made by our ML models. May 8, 2018 · sample size fed into shap. . Deep class shap. This guide shows how to install and use SHAP. TreeExplainer(gbt) shap_values = explainer Mar 10, 2021 · explainer = shap. shap_values(X_test,nsamples=100) A nice progress bar appears and shows the progress of the calculation, which can be quite slow. We calculate SHAP values for the test set using the shap_values method of the explainer. It helps interpret machine learning models. In this article, we will explore how to integrate SHAP with a linear SVC model from Scikit-learn using a Pipeline. Generate SHAP Values for Images: Compute SHAP values for a subset of images. Note that with a linear model the SHAP value for feature i for the prediction [Math Processing Error] f (x) (assuming feature independence) is just [Math Processing Error] ϕ i = β i (x i E [x i]). This works well for standard Shapley value maskers for models with less than ~15 features that vary from the background per sample. Jul 10, 2025 · explainer = shap. shap_values, I would assume it has something to do with number of features (columns) For example, I have over 1 million record with 400 raw features (continuous + unencoded categorical). Since we are explaining a logistic regression Apr 18, 2025 · TreeExplainer is a fast implementation of Tree SHAP, an algorithm specifically designed to compute SHAP values for tree-based machine learning models. Install SHAP can be installed from either PyPI or conda-forge: pip install shap or conda install SHAP (an acronym for SHapley Additive exPlanations) is a way to explain the predictions of a machine learning model, introduced by Lundberg and Lee in 2017 [1]. This makes it easier to calculate SHAP values for diverse data forms, enhancing interpretability across both structured and unstructured datasets. Nov 27, 2024 · Exploring SHAP for Global and Local InterpretabilityWhat is SHAP? SHAP (SHapley Additive exPlanations) is an explainability framework grounded in game theory. The explainer needs some model function that produces an output from a given list of strings. IME) SamplingExplainer computes SHAP values under the assumption of feature independence and is an extension of the algorithm proposed in “An Efficient Explanation of Individual Classifications using Game Theory”, Erik Strumbelj, Igor May 29, 2025 · Learn how to use SHAP to transform your XGBoost models from black boxes into transparent, explainable systems that reveal exactly how each feature contributes to every prediction. Oct 30, 2022 · Explainer is a super class, which depending on args -- type of a model and data -- delegates the responsibility to calcualte SHAP values to concrete implementations of an explainer. As established in the prerequisite, familiarity The SHAP library allows users to automatically select the most suitable explainer based on their model type. Local API Examples These examples parallel the namespace structure of SHAP. shap_values(X_test) This segment of code calculates the SHAP values, which tell us how much each feature contributes to a specific prediction. Feb 1, 2024 · explainer = shap. X_test is the dataset used to Jul 23, 2025 · SHAP (SHapley Additive exPlanations) is a powerful tool for interpreting machine learning models by assigning feature importance based on Shapley values. May 19, 2024 · shap_values = explainer. At the end, we get a (n_samples,n_features) numpy array. This is my second article on… WHAT is SHAP? SHAP(SHapley Additive exPlanations) values are used to explain the output of any machine learning model. DeepExplainer: Tailored for deep learning models using TensorFlow or Keras. It was named in honour of Aug 12, 2022 · For the code given below, I am getting different bar plots for the shap values. And next, you can pass in the dataset to the explainer to understand how each of the features contributes to predicting the model output. SHAP values are based on game theory and assign an importance value to each feature in a model. For example, if you create a tree-based model you should create a Tree SHAP explainer. LinearExplainer(model, masker = masker) This is akin usual train/test paradigm, where you train your model (and explainer) on train data, and try to predict (and explain) your test data. SHAP stands out for: Global Interpretability: Identifying features that impact the model’s predictions across the dataset. API Reference This page contains the API reference for public objects and functions in SHAP. Linear(model, masker, link=CPUDispatcher (<function identity>), nsamples=1000, feature_perturbation=None, **kwargs) Computes SHAP values for a linear model, optionally accounting for inter-feature correlations. Explainer(model, masker=None, link=CPUDispatcher (<function identity>), algorithm='auto', output_names=None, feature_names=None, linearize_link=True, seed=None, **kwargs) Uses Shapley values to explain any machine learning model or python function. shap_values(X_test_norm) Since the SHAP values are calculated for each data instance, the summary plot provides a good estimate of global feature importance. com SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. A primary use of SHAP is to understand how variables and values influence predictions visually and quantitatively. This is the primary explainer interface for the SHAP library. Assuming features are The practical implementation of SHapley Additive exPlanations (SHAP) utilizes the popular shap Python library. Obtain the SHAP values with the explainer and obtain the graphs you Jun 28, 2023 · SHAP Values in Machine Learning SHAP values are a common way of getting a consistent and objective explanation of how each feature impacts the model's prediction. But that is a misconception. It provides exact computation of SHAP values for Dec 14, 2021 · Traditionally, critics of machine learning and deep learning say even they get accurate predictions, we are creating "black box" models. Oct 23, 2023 · explainer = KernelExplainer(model. This computes the SHAP values for a linear model and can account for the correlations among the input features. SHAP (SHapley Additive exPlanation) is a game… Apr 18, 2025 · Explainer Base Class Relevant source files The Explainer Base Class is the foundation of SHAP's explanation system, providing a unified interface for all algorithm-specific explainers. Install SHAP can be installed from either PyPI or conda-forge: Oct 31, 2024 · It is important to explain the output of any machine learning model. explainers Exact explainer GPUTree explainer Permutation explainer Once installed, obtaining SHAP values is easy, you just have to: Train a model (as you would do even if you were not using SHAP). May 17, 2021 · shap_values = explainer. Partition class shap. They are based on concepts from cooperative game theory, specifically the Shapley value, which fairly distributes the “payout” (in this case, the prediction) among the “players” (features) based on their contributions. It also works well for Owen values from hclustering Nov 30, 2024 · Discover how to build and apply SHAP and Python for explainable AI models, improving model transparency and trustworthiness. Partition(model, masker, *, output_names=None, link=CPUDispatcher (<function identity>), linearize_link=True, feature_names=None, **call_args) __init__(model, masker, *, output_names=None, link=CPUDispatcher (<function identity>), linearize_link=True, feature_names=None, **call_args) Uses the Partition SHAP method to explain the output of any Jul 3, 2024 · Create a SHAP Explainer: Use SHAP to create an explainer object with the model and image masker. These explainers are appropriate only for certain types or classes of algorithms. Exact class shap. This library provides efficient implementations of various SHAP algorithms, including KernelSHAP and TreeSHAP, along with powerful visualization tools. Tree(model, data=None, model_output='raw', feature_perturbation='interventional', feature_names=None, approximate=False, **deprecated_options) Uses Tree SHAP algorithms to explain the output of ensemble tree models. shap_values(X_test) is expensive and most probably is a kind of an exact algo to calculate Shapely values out of a function. Linear class shap. We'll provide an overview of SHAP, discuss how to build an SVC model, and illustrate how to interpret the model with SHAP. However, since it completely enumerates the space of masking patterns it has O (2 M) complexity for Shapley values and O (M 2) complexity for Owen values on a Welcome to the SHAP documentation SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. Jun 5, 2020 · This article is a guide to the advanced and lesser-known features of the python SHAP library. In this example, I have a dataset of 1000 train samples with 9 classes and 500 test samples. Visualization Tools: Offers a variety of visualization options, including summary plots, dependence plots, and force plots, to intuitively represent feature contributions and interactions. KernelExplainer: Model-agnostic, works on any black-box model but is slower. This tutorial will guide you through understanding, implementing, and optimizing SHAP values to gain insights into your models’ decision-making processes. Tree SHAP is a fast and exact method to estimate SHAP values for tree models and ensembles of trees, under several different possible May 23, 2025 · There are several types of SHAP explainers: TreeExplainer: Fast and exact for tree-based models like XGBoost, LightGBM, and CatBoost. Each element is the shap value of that feature of that record. It tells us how much each input (feature) is helping or hurting the final See full list on towardsdatascience. Apr 18, 2025 · Deep Learning Explainers are specialized components in the SHAP library designed to explain predictions from neural network models. Machine learning and deep learning models can be interpretable. Aug 7, 2024 · Explainer Classes: SHAP provides specific explainer classes such as TreeExplainer, DeepExplainer, and KernelExplainer, tailored to different types of models. Below is an example of how to utilize SHAP’s universal explainer with Python. I then use the random f Mar 30, 2020 · Tree SHAP is an algorithm to compute exact SHAP values for Decision Trees based models. Explainer class shap. explainers. shap_values(X_test) summary_plot(shap_values, X_test) The SHAP summary plot gives an overview of how each feature impacts the predictions. Tutorial creates various charts using shap values interpreting predictions made by classification and regression models trained on structured data. 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. Christoph Molnar, in his book "Interpretable Machine Learning", defines Mar 20, 2022 · shap. SHAP (SHapley Additive exPlanations) is a powerful tool in the machine learning world that draws its roots from game theory. Model interpretation is a very active area among researchers in both academia and industry. It assigns each feature in a model an importance score based on its contribution to the prediction. Sampling(model, data, **kwargs) This is an extension of the Shapley sampling values explanation method (aka. shap. It serves as an abstract base class that handles common functionality like model wrapping, masker initialization, and result processing, while delegating algorithm-specific logic to its subclasses. Each SHAP explainer estimates the contribution of each feature to the prediction, which can then be 6. For MLflow's built-in SHAP integration provides automatic model explanations and feature importance analysis during evaluation. Dec 29, 2020 · Explaining aggregate feature impact with SHAP summary_plot While SHAP can be used to explain any model, it offers an optimized method for tree ensemble models (which GradientBoostingClassifier is) in TreeExplainer. Sentiment Analysis with Logistic Regression This gives a simple example of explaining a linear logistic regression sentiment analysis model using shap. shap_values = explainer. What is SHAP? SHAP is a method that helps us understand how a machine learning model makes decisions. Sampling class shap. It provides insights into how much each feature in a dataset contributes to a particular prediction, making complex models more understandable and transparent. Explainer(model) shap_values = explainer(X) First, you can create an explainer by passing the model to the Explainer function. Each object or function in SHAP has a corresponding example notebook here that demonstrates its API usage. SHAP (SHapley Additive exPlanations) values help you understand what drives your model's predictions, making your ML models more interpretable and trustworthy. Tree class shap. The API of SHAP is built along the explainers. TreeExplainer(rf_classifier) In this line of code, we are setting up an 'explainer' to help us understand how our machine learning model (rf_classifier), makes its predictions. This explainer is specifically designed for tree-based models like XGBoost. There are also example notebooks available that demonstrate how to use the API of each object/function. Mar 30, 2020 · Kernel SHAP is a model agnostic method to approximate SHAP values using ideas from LIME and Shapley values. Explanation Sep 1, 2022 · explainer = shap. Deep(model, data, session=None, learning_phase_flags=None) Meant to approximate SHAP values for deep learning models. PermuationExplainer: Model-agnostic explainer that iterates through forward and reverse permutation of inputs. Lundberg and Lee, NIPS 2017 showed Exact explainer This notebooks demonstrates how to use the Exact explainer on some simple datasets. Remember that shap values are calculated for each feature and for each Jun 1, 2025 · SHAP (SHapley Additive exPlanations) is a Python library. The Exact explainer is model-agnostic, so it can compute Shapley values and Owen values exactly (without approximation) for any model. Sep 19, 2024 · Well, SHAP values are built on that very idea. SHAP SHAP ’s goal is to explain machine learning output using a game theoretic approach. SHAP is a local explanation method - it gives an explanation for why a model made a specific prediction We initialize a SHAP TreeExplainer with the trained XGBoost model. Exact(model, masker, link=CPUDispatcher (<function identity>), linearize_link=True, feature_names=None) Computes SHAP values via an optimized exact enumeration. SHAP (Shapley Additive Explanations) is a popular technique for this. It takes any combination of a model and masker and returns Jul 14, 2025 · SHAP (SHapley Additive exPlanations) provides a robust and sound method to interpret model predictions by making attributes of importance scores to input features. Create a SHAP explainer, which will depend on the type of model you have created, as we have mentioned above. Nov 11, 2025 · SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. 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). It is based on an example of tabular data… May 20, 2022 · Implementation in shap In shap, Owen values are implemented by the partition explainer, which is called by default for text models. KernelExplainer, and what is the guiding principal to choose these samples; number of samples fed into function explainer. For example, you should use the TreeExplainer for tree shap. predict, X_train) shap_values = explainer. What you’ll learn: Understanding the core concepts and Feb 26, 2025 · Learn how to interpret machine learning models using SHAP values with hands-on Python examples and step-by-step explanations. We'll walk through the process of generating SHAP explanations for a sample model. shap. With a couple of lines of code, you can quickly visualize the aggregate feature impact on the model output as follows explainer = shap. The source notebooks are available on GitHub. shapAdditiveExplainer: This explains models that have only first-order effects. These values represent the feature importances for each instance in the test set. Nov 14, 2024 · In this tutorial, we’ll walk through how to extend SHAP (SHapley Additive exPlanations) to interpret custom-built machine learning models… Dec 9, 2024 · Introduction Mastering Model Explainability with SHAP (SHapley Additive exPlanations) Values is an essential skill for any data scientist working with machine learning models. This shows petal length and width have the largest SHAP values, so they have the biggest influence driving predictions. In the following code snippet, I present an example. This tutorial gives a gentle introduction to SHAP explanations of machine learning predictions, without too much technical detail. hvtqgc bqakv fsmxcns rfeeuv mcznz uwbsow lgnqf nfhlw pxq vin fljm nsnscqu gbc uksre rbbvx