When I use SHAP for classification problem, it shows an output that is not 0 or 1. How can I overcome this?

Question

I'm using Pima Indians Diabetes Database(https://www.kaggle.com/uciml/pima-indians-diabetes-database). I made predictions using XGboost and I'm trying to analyze the features using SHAP.

However when I use force_plot with just one training example(a 1x8 vector) it shows that my output is -2.02. This is a classification problem, I shouldn't be seeing such a value. I'm new in SHAP and I don't know what the problem is.

Here is my code:

import numpy as np
import xgboost as xgb
import sklearn as skl
import shap

dataset=np.loadtxt("diabetes.csv", delimiter=",")
X=dataset[:,0:8]
Y=dataset[:,8]
seed=7
test_size=0.33
X_train, X_test, y_train, y_test=skl.model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)

shap.initjs()
model=xgb.XGBClassifier()
model.fit(X_train, y_train)

predictions=model.predict(X_test)
accuracy=skl.metrics.accuracy_score(y_test, predictions)
print(accuracy*100)

explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_train)
shap.force_plot(explainer.expected_value, shap_values[0,:].reshape(1, 8), X_train[0,:].reshape(1, 8))

Accuracy of my model is: 77,95.

Answer 1

The default link function is the identity, so you are seeing log-odds rather than probabilities. To see the probability, try adding link='logit' to your call of force_plot like so:

shap.force_plot(explainer.expected_value,
                shap_values[0,:].reshape(1, 8),
                X_train[0,:].reshape(1, 8),
                link='logit')

You can read more at the SHAP documentation site.