shap.KernelExplainer¶ class shap.KernelExplainer (model, data, link=<shap.utils._legacy.IdentityLink object>, **kwargs) ¶ Uses the Kernel SHAP method to explain the output of any function. Kernel SHAP is a method that uses a special weighted linear regression to compute the importance of each feature.
18.01.2019 · The model is a RandomForestClassifier from sklearn, when I try to create the dependence plot from a single value: shap.dependence_plot("S", shap_values, x_train) I get the following error: -----...
Kernel SHAP uses a specially-weighted local linear regression to estimate SHAP values for any ... AttributeError: 'list' object has no attribute 'shape'.
shap.KernelExplainer¶ class shap.KernelExplainer (model, data, link=<shap.utils._legacy.IdentityLink object>, **kwargs) ¶. Uses the Kernel SHAP method to explain the output of any function. Kernel SHAP is a method that uses a special weighted linear regression to …
KNeighborsRegressor" and "sklearn.svm.SVR" models, with later obtaining explainer objects (which contain the shap values, the base values, and the data used), ...
27.08.2018 · My setup is: OS: Ubuntu 18.04.1 LTS shap version: shap==0.24.0 installed with pip3 install --user shap When I try to execute the following code: from sklearn import svm from sklearn import datasets import shap clf = svm.SVC() iris …
Uses the Kernel SHAP method to explain the output of any function. ... is None then the Explanation objects produced by this explainer will not have any ...
The shap_values object above is a list with two arrays. The first array is the SHAP ... use Kernel SHAP to explain test set predictions k_explainer = shap.
Welcome to the SHAP documentation . 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). Install
Welcome to the SHAP documentation . 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).
Jan 09, 2014 · This answer is useful. 7. This answer is not useful. Show activity on this post. list object in python does not have 'shape' attribute because 'shape' implies that all the columns (or rows) have equal length along certain dimension. Let's say list variable a has following properties: a = [ [2, 3, 4] [0, 1] [87, 8, 1]] it is impossible to define 'shape' for variable 'a'.
Aug 27, 2018 · My setup is: OS: Ubuntu 18.04.1 LTS shap version: shap==0.24.0 installed with pip3 install --user shap When I try to execute the following code: from sklearn import svm from sklearn import datasets...
Nov 06, 2019 · This has to go back to the Vapnik-Chervonenkis (VC) theory. It says mapping into a higher dimensional space often provides greater classification power. See my post “Dimension Reduction Techniques with Python” for further explanation. The common kernel functions are Radial Basis Function (RBF), Gaussian, Polynomial, and Sigmoid.