I have a dataset of machines that produce plastic parts. A camera evaluates whether a plastic part was produced correctly or not (binary classification). I'm trying to figure out which factors influence a part being wrongly produced. E.g. I have different temperature values of the machine parts during the production.
I'm using a Random Forest to classify the data. The test dataset is being recognized quite well. The next step is to figure out which values lead to a wrongly produced part (e.g. when temperature > 150K: Part is broken). I've searched the internet but I couldn't find any information about this.
At the moment I'm trying a brute force method where I simply generate a test dataset where I go through different value ranges. But so far everything is classified as wrongly produced part.
Are there other methods I can use to get the values?