I got some question about the "standard" parameter from a random forest. Following I write my understanding about these parameters. I would be glad if I could confirm my understanding or correct it. :) For your information, I'm using scikit-learn.
max_depth: Thats the depth of a tree. In case of execution-performance it is good to set a limit. And for the model-performance it is also good, because of overfitting.
max_features: With this parameter I specifie der number of features with which tree is build. My question: If the parameter is set (for example 0.75), each tree includes 75% of all features, but every tree is build with different 75% feature?
min_samples_leaf: The minimal number of samples per leaf. Is this an important feature and why?
n_estimators: The number of trees. Is there a good default value for this parameter? Here are usually more trees better, right?
Are these the most important parameters or have I forgotten some?