Random Forest - Explanation Parameter 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?
You need to be very careful with your assumptions about "important" parameters. All the parameters have their function, that is precisely why they are implemented on the first place. If with importance you mean potential to affect your outputs/efficiency, perhaps there are some more relevant than others but most likely this relevance will also depend on the nature of your task and you data.

Having said that, I recommend you navigate through the scikit-learn documentation which is usually helpful and it contains a description including all these parameters: 


http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html


Moreover, they usually include nice and simple usage examples that can be helpful: here and here.

It is important that you practice and practice and you will start to get a feeling about the importance of each parameter and its relevance to the task at hand.

Finally, there are hundreds of blogs with nice tutorials that will give you a little bit more of insights about parameter tuning and "importance" ;) 

You can try this once for example. 

Good luck!