Hi Eugene, AFAIK, the current implementation of MultilayerPerceptronClassifier have some scalability problems if the model is very huge (such as >10M), although I think the limitation can cover many use cases already.
Yanbo 2015-12-16 6:00 GMT+08:00 Joseph Bradley <jos...@databricks.com>: > Hi Eugene, > > The maxDepth parameter exists because the implementation uses Integer node > IDs which correspond to positions in the binary tree. This simplified the > implementation. I'd like to eventually modify it to avoid depending on > tree node IDs, but that is not yet on the roadmap. > > There is not an analogous limit for the GLMs you listed, but I'm not very > familiar with the perceptron implementation. > > Joseph > > On Mon, Dec 14, 2015 at 10:52 AM, Eugene Morozov < > evgeny.a.moro...@gmail.com> wrote: > >> Hello! >> >> I'm currently working on POC and try to use Random Forest (classification >> and regression). I also have to check SVM and Multiclass perceptron (other >> algos are less important at the moment). So far I've discovered that Random >> Forest has a limitation of maxDepth for trees and just out of curiosity I >> wonder why such a limitation has been introduced? >> >> An actual question is that I'm going to use Spark ML in production next >> year and would like to know if there are other limitations like maxDepth in >> RF for other algorithms: Logistic Regression, Perceptron, SVM, etc. >> >> Thanks in advance for your time. >> -- >> Be well! >> Jean Morozov >> > >
