Partial dependency for random forest when predictive accuracy is bad I have a relatively simple question which is just that if I run a random forest regression model and then use partial dependency plots to understand the influence the features have on prediction of the independent variable, are the results of the partial dependency less valid if the model has worse predictive accuracy?
I was thinking that if the R squared is relatively low, that means that not all of the variance in the outcome variable is explained by the explanatory variables, but that does not necessarily mean that the effects of the inputted variables are invalid and hence interpretation of partial dependencies could remain the same?

A partial dependence plot (PDP) should be valid as a visualization of the marginal effect that your features have on the predicted outcomes of your model, regardless of the predictive accuracy of the model itself. PDPs are visual descriptions of the role that a feature plays in the predictions that a model makes. If the model isn't performing particularly well, the PDP will still show you how a certain feature contributed to the model's predictions.
I suppose the issue comes if you were to try and interpret your model (and corresponding PDPs) as a description of the real world situation it is trying to model or the outcome it is trying to predict. It's important to treat partial dependence plots as descriptions only of the model itself (especially if the predictive accuracy is poor), because they don't really attempt to tell you anything about any causal relationship between the variables in your model.
