I want to calculate shap values from a sklearn pipeline with a preprocessor and a model. When i do it with the code below I get 0 for all shape_values def creat
I want to use the SHAP-DeepInterpeter on the Braindecode Shallow_FBCSP-Model which is based on pytorch. The training and testing works perfectly fine on the mod
I want to do a simple shap analysis and plot a shap.force_plot. I noticed that it works without any issues locally in a .ipynb file, but fails on Databricks wit
After fitting a xgboost model (model_n) I try to run the code below to obtain shap-values, where trainval is a dataframe with my traindata without the Y variabe
When displaying summary_plot, the color bar does not show. shap.summary_plot(shap_values, X_train) I have tried changing plot_size. When the plot is higher th
I have this code in visual studio code: import pandas as pd import numpy as np import shap import matplotlib.pyplot as plt import xgboost as xgb from sklearn.m
Used the following Python code for a SHAP summary_plot: explainer = shap.TreeExplainer(model2) shap_values = explainer.shap_values(X_sampled) shap.summary_plot
I am trying to explain a regression model based on LightGBM using SHAP. I'm using the shap.TreeExplainer(<lightgbm model>).shap_values(X) method to get
I'm wondering if there's a way to change the order the features in a SHAP beeswarm plot are displayed in. The docs describe "transforms" like using shap_values.
When calculating local_accuracy from metrics.py I got the following error : NameError: name 'pickle' is not defined from shap.benchmark import metrics metrics.l
I have a causal inference model with featurizer=PolynomialFeatures(degree=3) which includes a degree 3 polynomial in X variable. I get the plot for interpretab
The Paper regarding die shap package gives a formula for the Shapley Values in (4) and for SHAP values apparently in (8) Still I don't really understand the dif
samples.zip The sample zipped folder contains: model.pkl x_test.csv To reproduce the problems, do the following steps: use lin2 =joblib.load('model.pkl') to loa
samples.zip The sample zipped folder contains: model.pkl x_test.csv To reproduce the problems, do the following steps: use lin2 =joblib.load('model.pkl') to loa
In a typical Shapley value estimation for a numerical regression task, there is a clear way in which the marginal contribution of an input feature i to the fina