What's the interpretation of the `partial_dependence` function output?

Question

I'm using scikit-learn 1.0.2 and the partial_dependence function (which should correspond one to one with the plot_partial_dependence) imported like this:

from sklearn.inspection import partial_dependence  

and I call it as follows in my code (same as in the documentation example):

out = partial_dependence(classifier, X_train, ['feature1'])

here out is a tuple containing the predictions and the grid values. I only care about the predictions. What is not clear to me is whether the out[0] or predictions are the fraction of the 'feature1' on the overall prediction OR it is the full prediction result. I also don't understand why the results are so different when switching between response_method='predict_proba' and response_method='decision_function' for example for a SVM binary classifier assuming the decision boundary is 50% I would expect these two to output similar values but the difference is huge.

Can anyone explain?