Assume we train a linear model to predict a numeric outcome.
A feature's model weight would essentially quantify me how much the outcome variable increases for each increase in the predictor's value.
I very recently stumbled upon partial dependence. If I understand it correctly, the partial dependence of a variable quantifies the influence of this variable on the dependent variable with all other variable marginalized out, i.e. all other things constant.
Am I right in thinking that partial dependence of an independent variable measures something very similar to a feature weight?