How to compare two random forests in scikit-learn? With most learning algorithms, one can compare the models resulting from applying the algorithm on samples of data by the parameters of the models. For example, one can compare two logistic regression models by comparing the learned model parameters (I'm not referring to the hyperparameters here).
I would like to do that with two random forest models trained with scikit-learn's random forest algorithm. However, I do not see any properties or methods that give me access to parameters that can be used to draw a comparison.
How would you compare two random forest models?

In logistic regression, the coefficients of a variable indicates the expected change in log odds of observed outcome, per unit change in that variable. So by looking at these model parameters, one can get a feeling which variables are most crucial in increasing/decreasing the probability of observing the outcome.
Something similar in random forest is the feature importance. In scikit-learn, it is possible to extract the mean decrease in impurity for each feature. So when this value is large, it means that splitting on this feature will on average more likely result in pure groups. It resembles large positive or large negative coefficients in the logistic setting.
https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html
