SHAP library python = how to get the shap values of each feature

I am currently working with the SHAP library, I already generated my charts with the avg contribution of each feature, however I would like to know the exact value that is ploted on the chart

import numpy as np  import pandas as pd    from sklearn.linear_model import LinearRegression  from sklearn.datasets import load_boston  import shap      boston = load_boston()  regr = pd.DataFrame(boston.data)  regr.columns = boston.feature_names  regr['MEDV'] = boston.target    X = regr.drop('MEDV', axis = 1)  Y = regr['MEDV']    fit = LinearRegression().fit(X, Y)    explainer = shap.LinearExplainer(fit, X, feature_dependence = 'independent')  # I used 'independent' because the result is consistent with the ordinary   # shapely values where `correlated' is not    shap_values = explainer.shap_values(X)    shap.summary_plot(shap_values, X, plot_type = 'bar')  

How can I get the exact values that are depicted of the chart?

https://stackoverflow.com/questions/65837159/shap-library-python-how-to-get-the-shap-values-of-each-feature January 22, 2021 at 07:15AM