Shap summary plot feature names. dependence_plot function.
Shap summary plot feature names. May 8, 2021 · My primary question: How can I figure out which feature in ['Age', 'Workclass', 'Education-Num', 'Marital Status', 'Occupation', 'Relationship', 'Race', 'Sex', 'Capital Gain', 'Capital Loss', 'Hours per week', 'Country'] applies to which number in each row of shap_values? >>> shap_values[0] SHAP offers powerful visualizations that aggregate information across many instances, helping us understand global feature importance and dependencies. Explanation or shap. SHAP Summary Plot (dot version). Bar plot shap. It is used for example if you want to override the column names of a panda data frame, or you are just passing a numpy array for your data. # we use whole of X data from more points on plot Dec 4, 2023 · I want to decide the order of the feature inside the shap. 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 Jun 6, 2019 · That means the names of the features for each column are the same as for your data matrix. SHAP (SHapley Additive exPlanations) values help you understand what drives your model's predictions, making your ML models more interpretable and trustworthy. Apr 5, 2022 · Now I would like to get the mean SHAP values for each class, instead of the mean from the absolute SHAP values generated from this code: shap_values = shap. summary_plot(shap_values, train_x. Jan 3, 2021 · shap. XGBClassifier(). By default, the size is auto-scaled based on the number of features that are being displayed. So this summary plot function normally follows the long format dataset obtained using shap. savefig('shap. I have two datasets: A SHAP value dataset containing the SHAP values for each data point in my original dataset. . 4. The Apr 13, 2022 · On the other hand, I want to use an alternative set of human-readable labels in my shap plots (summary plots, bar plots, swarm plots, etc). The color shows whether the original value of the feature was high or low for that instance. It’s useful for model comparison or selecting features for feature engineering. summary_plot Here is a code that is working, I want to learn to change the order of the variable instead of having ordered in list of impor Nov 26, 2024 · Discover how to use SHAP for feature importance visualization in data science and machine learning with our step-by-step guide. round(2 May 9, 2021 · using my XGBoost data, to make a dataframe of feature_names and their SHAP importance, as they would appear in a SHAP bar or summary plot. model_selection import train_test_split import Feb 1, 2024 · Equipped with this invaluable tool, she felt confident in aiding the bank in making equitable loan decisions. What is the reason Sep 20, 2024 · shap. 2 Industry Example: Bank Loan Approvals For example, the summary plot may reveal: Credit_History: The most influential feature, with good credit history strongly 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. datasets import make_classification from shap Dec 10, 2020 · You can add a title by using show=False option then add title through plt. Feb 9, 2024 · let us suppose we have the following simplified code: import pandas as pd import shap from sklearn. I'd like to produce a stacked bar plot showing overall feature importance like this example. summary_plot(shap_values, X_test) This technique is widely used in finance, healthcare, and other sensitive domains where explainability is critical. Apr 7, 2023 · shap. It is the SHAP approach. Two fundamental plots for this purpose are the Summary Plot and the Dependence Plot. ensemble import RandomForestRegressor from sklearn. Tutorial creates various charts using shap values interpreting predictions made by classification and regression models trained on structured data. This reveals not just which features are important, but how their values affect the prediction Dec 18, 2024 · shap. summary_plot(shap_values, X_test. The summary_plot function is cutting off the names of the features on the y axis. 2. May 17, 2021 · Neural networks are fascinating and very efficient tools for data scientists, but they have a very huge flaw: they are unexplainable black boxes. g. How can I know to which class the 0,1 & 2 from the original correspond? Jan 17, 2022 · For analysis of the global effect of the features we can use the following plots. The x-axis indicates the SHAP value, which represents the contribution of a feature to the prediction. feature_names) This plot provides an overview of which features are most important and how their values impact the model’s May 29, 2025 · SHAP’s summary plot reveals these global insights by aggregating feature impacts across all predictions, showing us not just which features are important, but how they behave across different value ranges. columns, np. The summary plot (a sina plot) uses a long format data of SHAP values. , passing in a dictionary mapping raw feature names to feature labels? # The first argument is the matrix of SHAP values (it is the same shape as the data matrix)# The second argument is the data matrix to explain (a pandas dataframe or numpy array)# The third argument is provide feature names (optional)# The forth argument is to deferred the showing function if you want to customize the plotshap. shap. show() Best Practices and Optimization Performance Considerations Use SHAP values to understand the importance of each feature in the model’s decision-making process Use partial dependence plots to visualize the relationship between a specific feature and the predicted outcome Oct 14, 2018 · shap. summary(shap_long_iris) # option of dilute is offered to make plot faster if there are over thousands of observations # please see documentation for details. Here we use the Shap library to evaluate the features in our model. Any help would be greatly appreciated. show() Jun 5, 2020 · This article is a guide to the advanced and lesser-known features of the python SHAP library. Is there an easy way to do this via e. May 28, 2025 · Replace 'feature_name' with the name of the feature you want to analyze. summary. png') This works okay and creates a plot that looks like: This looks okay but there is a couple problems. Both the SHAP values and feature importance values have good consistency across the 5 k-fold splits. values. Note that the feature values are show in gray to the left of the feature names. Sep 22, 2018 · The feature_names option is just a way to pass the names of the features for plotting. Understand feature impact, train models, and visualize insights with SHAP force plots and summary plots. The XGBoost model does provide a measure of feature importance. Passing a single float will cause each row to be that many inches high. title("bar") plt. Aug 21, 2019 · In the SHAP summary plot, you'll see "Class 0", "Class 1" and "Class 2" instead of "A", "B" and "C". wrap1. Explanation Jun 28, 2023 · Overall, SHAP values provide a consistent and objective way to gain insights into how a machine learning model makes predictions and which features have the greatest influence. summary_plot(shap_values, X_train3, feature_names= X_train3. In this tutorial, I will walk you through each step of SHAP-aided machine learning for identifying elemental combinations for a potential thermoelectric with an ideal combination of high power factor (PF) and low thermal conductivity (κ or kappa). adult() model = xgboost. Apr 5, 2022 · I am working on a binary classification using random forest model, neural networks in which am using SHAP to explain the model predictions. Apr 18, 2021 · So I am generating a shap summary plot like so: explainer = shap. It takes in account the absolute SHAP value, so it does not matter if the feature affects the prediction in a positive or negative way. The first summery plot outputs a simple bar chart with features listed in order of importance. 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. dependence_plot function. mean(0). SHAP Force Plot The force plot provides a detailed view of how each feature contributes to a single prediction. Dependance Plot This helps capture feature interactions as well. summary_plot(shap_values[1], X_test, feature_names=data. Interpreting Black Box Models SHAP also supports interpretation of other models like decision trees, random forests, or even Jan 28, 2025 · Learn how to use SHAP on Databricks to explain machine learning predictions. expected_value[0]. Say, in NLP where you have a tokenizer step for feature_names (i. force_plot for 100 samples in train-set: May 15, 2019 · shap. 文章浏览阅读3. The SHAP values could be obtained from either a XGBoost/LightGBM model or a SHAP value matrix using shap. Oct 12, 2019 · shap. This means it contains values of 0 or 1. py", line 41, in <module> main() API Reference This page contains the API reference for public objects and functions in SHAP. This reveals not just which features are important, but how their values affect the prediction Nov 27, 2024 · Exploring SHAP for Global and Local InterpretabilityOutput: A horizontal bar chart where: The length of each bar indicates the overall importance of the feature. 5. Apr 14, 2021 · What is the interpretation of the following shap. summary_plot(shap Nov 27, 2024 · Exploring SHAP for Global and Local InterpretabilityOutput: A horizontal bar chart where: The length of each bar indicates the overall importance of the feature. In fact, they don't give us any information about feature importance. abs(shap_values. summary_plot(shap_values, features=test, feature_names=X_columns) I need shap to show me the feature names on the plot, instead of the number of the features, as it is showing now: Jun 5, 2020 · I have rather long feature names in the data set I am using. If you want to start with a model and data_X, use shap. Sep 2, 2018 · Then I create the shap values, use these to create a summary plot and save the create visualization. summary_plot(shap_values, features=features, feature_names=feature_names, class_names=class_names) The plotting function will then add the class names to the plot's legend. summary_plot(shap_values, X. While it is easy for some plots, we have to get crafty for others. columns. May 6, 2024 · I've found a solution to my problem with cropped feature names and poorly scaled images in SHAP plots. To put it simply, a SHAP plot serves as a summary visualization for complex machine learning models, such as Random Forest. TreeExplainer(trained_best_model) shap_values = explainer Jan 1, 2021 · I believe it works as follows: shap_values need to be averaged. Everything works fine if I save the plot as plt. Explainer(model, X_train) shap_values = explainer(X_test) # Generate SHAP summary plot shap. 29. Jul 14, 2025 · Output: Bar plot of mean SHAP values This is helpful when identifying which features are generally more important. fit(X, y) explainer = shap. png'). shap_values(X_test) shap. If you want to use a self-derived Mar 29, 2024 · Hello, I’m working on recreating the summary plot from the SHAP library using Plotly. plots. Dec 2, 2021 · I would like to get the correct (meaningful) feature names into my Shap summary plot with shap_interaction_values for all the model features, rather than generic (numeric, generic) feature names (e. I would like the full feature name to be displayed on y-axis. summary_plot for all samples in test-set: shap. title(): shap. Although the solution isn't perfect, as it still truncates very long feature names on the y-axis, it significantly improves the overall appearance and readability of the plots. TreeExplainer(model, X) shap_values = explainer(X) feature_names = [ a + ": " + str(b) for a,b in zip(X. DataFrame (feature_names, columns = ['feature_names']) Does anybody have an experience, how to interpret shap_values? At first i thought, that the number of values are the number of features x number of rows. The original dataset, which includes the one-hot encoded values of the features. savefig('importance_scatter_xgb. 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 14, 2021 · The waterfall plot also allows us to see the amplitude and the nature of the impact of a feature. abs or shap_values. mean(0) to change how the ordering is calculated, but what I actually want is to put in a list of features or indices and have it order by that. We found that there is a reasonable similarity between the feature importance and the SHAP values, but with some differences in the ranked order. Feature 1, Feature 2, etc. savefig('test. It also allows seeing the order of importance of the features and the values taken by each feature for the sample. shap_values[0]) or a multi-row Explanation object that we want to summarize. Each point represents a SHAP value for a specific instance and feature. 4. Passing a pair of floats will scale the plot by that number of inches. max_displayint How many top features to include in the bar plot (default is 10). I followed the tutorial and wrote the below code to get the Sep 13, 2022 · How to go about extracting the numerical values for the shap summary plot so that the data can be viewed in a dataframe?: Here is a MWE: from sklearn. named_steps dict. The summary plot shows the impact of each feature on the model's output for all instances in the test set. Colors show whether the feature values are high (red) or low (blue). dependence_plot ¶ This notebook is designed to demonstrate (and so document) how to use the shap. We specify plot_type="bar" to create a bar plot that shows the mean absolute SHAP values for each feature, indicating their overall importance. KernelExplainer(model, X_test[:100,:]) shap_values = explainer. Fortunately, there is a powerful approach we can use to interpret every model, even neural networks. The problem is that every time I try to plot the shap values, I keep getting this error: 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. summary_plot(shap_values, X_test_scaled, feature_names=data. How to Implement SHAP Values in Python In this section, we will calculate SHAP values and visualize feature importance, feature dependence, force, and decision plot. Currently, in this form, the SHAP summary plot cannot be exploited. _waterfall. pdf', format='pdf', dpi=1200, bbox_inches='tight') some strange blue rectangles appear above the data points. May 29, 2025 · SHAP’s summary plot reveals these global insights by aggregating feature impacts across all predictions, showing us not just which features are important, but how they behave across different value ranges. words/n-grams) and an ML model for Jul 4, 2024 · Explainability using SHAP (for binary classification) Ask Question Asked 1 year, 2 months ago Modified 1 year, 1 month ago Mar 18, 2025 · Im running a random forest model and to get some feature importance and Im trying to run a SHAP analysis. From reading May 2, 2022 · SHAP plots are a bit tricky to customize unless you're willing to tinker with the source code, but the following will do: import xgboost import shap X, y = shap. TreeExplainer(model). If you have those names around somewhere as a list you can pass them to summary_plot using the feature_names argument. ). It visualizes the push and pull of SHAP values on the model’s base value (the Finally, we plot the SHAP values using the summary_plot() function. The horizontal position shows the SHAP value (impact), and the color indicates the original feature value (high or low). 2 Industry Example: Bank Loan Approvals For example, the summary plot may reveal: Credit_History: The most influential feature, with good credit history strongly Feb 24, 2025 · Implementation in Python: import shap # Create SHAP explainer explainer = shap. TreeExplainer(p plot_size“auto” (default), float, (float, float), or None What size to make the plot. I would appreciate any information to remove them Apr 7, 2022 · I'm wondering if there's a way to change the order the features in a SHAP beeswarm plot are displayed in. abs. showbool Hello! I have tried using the summary plot with the TreeExplainer in PyCharm and I cannot find a way to make the feature names visible. There are also example notebooks available that demonstrate how to use the API of each object/function. Features are ranked by their global importance (mean absolute SHAP value). TreeExplainer(trained_best_model) shap_values = explainer Oct 4, 2022 · Or use a predefined color palette: shap. I have a XGBoost trained. I think it could be improved, especially as the classes' names are stored in the model (field classes_). I have tried changing the plot size but it did not work. Explanation objects A single row of a SHAP Explanation object (i. It provides exact computation of SHAP values for tree ensembles with optimized implementations for popular libraries like XGBoost, LightGBM, CatBoost, and scikit-learn's tree-based models. This problem exists even though I get the correct (meaningful) feature Feb 12, 2022 · shap. Is there a way around this? I have tried several different versions of the shap package (0. values Nov 23, 2021 · I also tried to replace it with the following to see if I could at least recover my feature names: shap. summary_plot and shap. The plot suggests that hr had the greatest impact on the model output, followed by atemp, season, and hum. Nov 14, 2024 · shap. shap_values(X_test[:100,:]) fig = shap. Sep 10, 2019 · I am using SHAP to get a further idea about my model features meaning. Apr 21, 2023 · A cohort plot allows you to look at Shap features using higher granularity and get higher interpretability You can use more than 2 groups if you need, so there are no limitations here. The resulting plot will display the feature values on the x-axis and the corresponding SHAP values on the y-axis. Here, we see the prediction process for a specific instance. datasets. It aids in understanding how each feature influences the target variable. Oct 4, 2022 · Or use a predefined color palette: shap. classes_) Note that the model can be two different models if you use a pipeline, accessible via the pipeline. Following advice from how to extract the most important feature names? and How to get feature names of shap_values from TreeExplainer? specifically the comment by user Thoo, which shows how the values can be May 23, 2025 · This plot doesn’t show direction (positive/negative impact), but it effectively ranks features by overall importance. get_feature_names_out(), class_names=model. waterfall_legacy(explainer. summary_plot(shap_values, Xs, feature_names=names, plot_type="violin", show=False) plt. SHAP Dependence Plot ¶ To display the relationship between a feature’s values and its Shapley values, use the Documentation by example for shap. summary Dec 7, 2021 · print('%s, %0. It is based on an example of tabular data… The summary plot, with feature names displayed on the y-axis and Shapley values on the x-axis, uses blue and red to indicate low and high feature values, respectively. force_plot(shap_values[0], plot_cmap = "PkYg") Force plot with modified color palette by using predefined color palette (Image by the author) Conclusion This article showcased how to quickly customize SHAP plots. The summary plot effectively communicates which features have the most significant impact on the prediction. This is the code I am using: explainer = shap. 6 Dependence Plot Shows how the SHAP value of a single feature varies with its value. The 2nd plot again shows the features listed in order of importance but also how much, both positively and negatively the feature impacts the model. However, when I apply it, it returns SHAP chart just fine, but the name of the feature are like feature 1, Apr 19, 2024 · Visualizing Model Interpretability with SHAP SHAP Summary Plot The SHAP summary plot provides a comprehensive view of feature importance. 3k次,点赞10次,收藏12次。多分类任务中使用shap. Here's the solution: # SHAP Explanation explainer = shap. 3f ' %(col,score)) I have long feature names and I plot the beeswarm shapley plots and feature names get truncated. feature_names) This will give you a summary plot of SHAP values for your test set, showing the importance of each feature and how it Mar 6, 2021 · It makes one-versus-one plot against two features by plotting shap values of one feature and coloring the dots with respect to another interactive feature. The docs describe "transforms" like using shap_values. summary_plot展示各个特征对模型输出类别的重要性。_shap summary plot May 19, 2024 · SHAP Summary plot The SHAP waterfall plot provides a detailed breakdown of how each feature contributes to a single prediction. Jun 5, 2022 · Would someone be able to show me how to change the X axis (ideally automatically, as I have to make multiple plots for different data sets), so the data is less bunched up and the full plot is seen? Mar 25, 2021 · The training data has 10,000 instances with 82 features each, and the target variable has 31 different classes. columns) This is because X_train3 is already in the format of a numpy array and does not require calling of . - helenaEH/SHAP_tutorial Dec 9, 2021 · Colormap bar on shap summary plot not displaying properly Asked 3 years, 9 months ago Modified 3 years, 7 months ago Viewed 6k times MLflow's built-in SHAP integration provides automatic model explanations and feature importance analysis during evaluation. values). Jun 26, 2025 · In this plot, each point represents a SHAP value for a feature and instance. summary_plot(shap_values, X_test) Also, the plot labels the class as 0,1,2. numpy(), shap_values[0][0], feature_names = test_data. I aim to create a bee swarm plot of the SHAP values and assign different Tutorial on how to use the SHAP library to explain the feature importance with Shapley values. Apr 27, 2024 · I am using XGBoost with SHAP to analyze feature importance in a multiclass classification problem and need help plotting the SHAP summary plots for all classes at once. and paired with the feature names: pd. plot. summary_plot(shap_values, X_test, feature_names=feature_names, show=False) plt. Cohorts or dictionary of shap. toarray(), feature_names=model. values, plot_type="bar", class_names= class_names, feature_names = X. columns) By Jul 12, 2021 · shap. Local bar plot Passing a row of SHAP values to the bar plot function creates a local feature importance plot, where the bars are the SHAP values for each feature. e. to_numpy()[0:5, :], feature_names=list(train_x. Parameters: shap_valuesshap. 7. Summary plot: beeswarm shap. values), plot_type='bar') But that threw an error: Traceback (most recent call last): File "sklearn_model_runs. It uses an XGBoost model trained on the classic UCI adult income dataset (which is classification task to predict if people made over 50k in the 90s). it has retained 33 variables, however, when I use summary_plot or force_plot, it only gives me 20 variables in the graph. summary_plot(shap_values, features=X_test[:100,:], feature_names=feature_names, show=False) plt. bar(shap_values) Image by author Here the features are ordered from the highest to the lowest effect on the prediction. Oct 13, 2021 · I'm using pipeline to transform data and predict model and I want to apply SHAP after that. columns) In this plot, the impact of a feature on the classes is stacked to create the feature Feb 26, 2025 · Learn how to interpret machine learning models using SHAP values with hands-on Python examples and step-by-step explanations. summary_plot(shap_values, df, plot_type='bar') plt. force_plot in terms of outliers? Is it clear how the SHAP toolset could transparent the contribution of features concerning outliers/anomalies? shap. 2qpvx thdxv ca14n sz rdxnf lf tr1kr ejrh vajb yu