I am working on binary classification and trying to explain my model using SHAP framework.

I am using logistic regression algorithm. I would like to explain this model using both KernelExplainer and LinearExplainer.

So, I tried the below

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from shap import TreeExplainer, Explanation
from shap.plots import waterfall

X, y = load_breast_cancer(return_X_y=True, as_frame=True)

idx = 9
model = LogisticRegression().fit(X, y)
background = shap.maskers.Independent(X, max_samples=100)
explainer = KernelExplainer(model,background)
sv = explainer(X.iloc[[5]])   # pass the row of interest as df
exp = Explanation(
    sv.values[:, :, 1],         # class to explain
    sv.base_values[:, 1],
    data=X.iloc[[idx]].values,  # pass the row of interest as df
    feature_names=X.columns,
)
waterfall(exp[0])  

         

This threw an error as shown below

AssertionError: Unknown type passed as data object: <class
‘shap.maskers._tabular.Independent’>

How can I explain logistic regression model using SHAP KernelExplainer and SHAP LinearExplainer?