Ryan, I’m working on a proposal for the idea, and wondering whether hyper-parameter module should be flexible enough to support metrics with different correlations. E.g., if we use accuracy as a metric, then we want to find a model that maximises this metric; on the other hand, if we want to use some kind of error as a metric (like mean squared error), then we need to find a model that minimises the metric. So, again, the question is whether hyper-parameter module should be flexible enough to maximise some metrics and minimise others?
Best regards, Kirill Mishchenko > On 22 Feb 2017, at 21:10, Ryan Curtin <[email protected]> wrote: > > On Wed, Feb 22, 2017 at 05:07:39PM +0500, Kirill Mishchenko wrote: >> Hi, >> >> my name is Kirill. I’m interested in the contribution to the project >> “Cross-validation and hyper-parameter tuning infrastructure”. I have >> already gone through some starting steps, like building the code and >> running a few ML algorithms (more precisely, I have did it for Linear >> Regression and Logistic Regression). Now I’m going to read rigorously >> the wiki page "Design Guidelines” and to go through the interfaces in >> the code base . Are there any other suggestions how I can start to >> work on the project? Is there some way to make a related small >> contribution to the code base? > > Hi Kirill, > > The cross-validation and hyper-parameter tuning project is pretty new, > and there is not much in the way of existing bugs that will help > understand it since the project involves generating a completely new > piece of code for mlpack. > > I just opened some issues for the decision tree code today; maybe you > can find one of those interesting? > > https://github.com/mlpack/mlpack/issues > (the top 5 are related to decision trees, at least when I wrote this > email) > > I think one approach would be to use the various different classifiers > and functionality inside of mlpack, and then write some simple C++ > programs to do cross-validation or hyper-parameter tuning by hand. > Then, this could help make it more clear what the needs of the > hyper-parameter tuning module and cross-validation module would be. > > Maybe these pages are also helpful: > > http://www.mlpack.org/involved.html > http://www.mlpack.org/gsoc.html > > There are also other issues open in the Github issue tracker, and any > contributions of new techniques or efficiency improvements for existing > implementations are always welcome. > >> Briefly about myself. I am a PhD student working on Computational >> Humor. More precisely I’m working on the problem of finding/generating >> a humorous response given a textual input. My programming experience >> includes two summer internships in big Russian IT companies: in one I >> was programming in C# (SKB Kontur), in another I was a C++ developer >> (Yandex search). In daily life I use Python. I have taken the online >> course Machine Learning by Stanford (Coursera), as well as some other >> courses related to ML (AI by Berkeley (EdX), Deep Learning by Google >> (Udacity), and others). > > Wow, computational humor, that is very cool! There was a group that I > worked with briefly at Georgia Tech on computational humor: > > http://www.vip.gatech.edu/teams/humor-genome > > I gave a talk to that group on the mlpack collaborative filtering code, > and I think that one point they were using mlpack_cf as a recommender > system for jokes, but I am not sure what came of it. I will have to > ask... > > I always thought it would be interesting to use generative deep neural > networks to try and generate jokes. I don't think they would be good > jokes, but I think they would be funny for the same reason my favorite > comic Garkov is funny: > > http://joshmillard.com/garkov/ > > I'd be interested to hear more about what you are doing there, if you'd > like to elaborate. I think that is a very neat field. > > Thanks, > > Ryan > > -- > Ryan Curtin | "If it's something that can be stopped, then just try to > stop it!" > [email protected] | - Skull Kid _______________________________________________ mlpack mailing list [email protected] http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
