Decision-Tree-ALg | decision tree platform for different types | Machine Learning library

Yes, this can be sped up:

Sci-kit learn uses, by default, the gini impurity measure (see Giny impurity, Wikipedia) in order to split the branches in a decision tree. This usually works quite well and unless you have a good knowledge of your data and how the splits should be done it is preferable to use the Sci-kit learn default.

About max_depth: this is the maximum depth of your tree, you don't want it to be very large because you will probably overfit the training data.

About max_features: every time there is a split, your training algorithm looks at a number of features and takes the one with the optimal metric (in this case gini impurity), and creates two branches according to that feature. It is computationally heavy to look at all the features every single time, so you can just check some of them. max_features is then the number of features you look every time you create a pair of branches on a node.