# Shapley Values - How to interpret each value for each feature for a specific instance?

I am using Shap Values(the 'shap' module in python) to help me understand a bit better the relation between my features and my target. I am currently working on a binary classification problem. I know that the base line value can be calculated considering the proportion of negative(or positive class) in my training dataset. Also, I know that by default (at least for the algorithms that I've been working with) the shap values given by explainer.shap_values are in log-odds unit and that, in some way, gives a dimension on how impactful the features are in your target prediction.

My question is, considering the shap values "y" (in log-odds or transformed to the probability itself) how can i interpret its value? I mean, Can I say that for one instance/record, the value Z of the X feature incremented the predicted probability of the positive class by y percentual points (or should i say incremented the predicted probability of the positive class by base line value multiplied by y?)

I am missing the part where I can connect the shap values for each instance/feature to a measurable way of saying how impactul that feature value was for a particular instance and how to achieve it. Suppose that for a instance, I have 5 shape values (for each of the 5 features of the model) and the base line is given by a value = 0.2. From that, Is it possible to arrive at the predicted probability or something like what i mentioned above?

Depending on the explainer and whether feature_dependence="independent", you may be able to get the values in probability space directly with the option model_output="probability".
There is no simple way to transform the values "post-explanation" from log-odds space to probability space for individual features. However, you can get to the predicted probability by converting p = np.exp(np.sum(y))/(1.0+np.exp(np.sum(y))) where y are your shap values.