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It's pretty popular.
by blahi 10y ago
It's pretty popular.
- danielmorozoff 10y agoI haven't seen it, maybe just me. Which companies do you know of?
- blahi 10y agoEbay, Electronic Arts, Google, Microsoft, many financial institutions.
- danielmorozoff 10y agoAre these production systems? I'm a bit blown away that this can work with R. http://adv-r.had.co.nz/Performance.html http://adv-r.had.co.nz/Performance.html
- apathy 10y agoR is a glue language. The fast bits are always written in C++, FORTRAN, or wrapping a BLAS like LAPACK. R-vs-Python is almost never the problem in production. Interpreted-vs-compiled is almost always the issue. (I'm aware of Numba and similar efforts. Last time I tried it, it sucked on nuts. And Theano is a rather specialized bit that most people don't actually need.) JMHO. But I've never seen anyone dealing with truly huge data and inference problems that had the low-level bits in anything other than C++ or FORTRAN. I could imagine that Scala can do a pretty good job now, especially if you use Spark a lot. But R vs Python seems like a really stupid question. Use the one that has the libraries you need.
- blahi 10y agoThis. R vs. Python is a very stupid debate. Especially for speed. For example pandas is slower than data.table. There are plenty of ways to have performant models in R. If you are worried about performance in deployment, run H2O, for example, there are plenty others. There is also flashr, if you want to write your own algorithms, which swaps R's basic operators and data management with C++. Also google is doing to R what they did to javqscript with v8. Expect GA next year.
- sevenless 10y agoWhat is GA? I think the right answer is both. And probably Javascript, too.
- blahi 10y agoYes, those are production systems like I said. Ebay scores their search results. EA scores customer lifetime value, churn, marketing communications.
- rm999 10y agoI've put substantial amounts of R code into production - it's a nightmare. Both for development and operationally. I think 2-3 years ago R was still a superior language for ML/data science dev work. But Python's library support has really caught up is now mature. The policy I put in place on my current team is to minimize R in production, with Python and Scala preferred. R in some cases still has the best machine learning libraries, which is really the only reason I've found to use it in the production stack. Even then, I prefer to just keep it at a few lines of R code (load the data, build the model, handle errors, export the model). For analyzing data I love R and almost always prefer it to Python.
- eric_bullington 10y ago> R in some cases still has the best machine learning librari Would you mind naming some specific machine learning techniques that are still better in R? I've been studying machine learning and linear algebra the past few months, and I'd love to have a try at implementing one myself in Python, as a learning exercise.
- apathy 10y agonot the OP, but glmnet is still the standard for lasso'ing, and I'm not aware of a Python implementation for post-selection inference and statistical testing yet. That said, the secret sauce in all of those is FORTRAN.
- eric_bullington 10y agoThanks for the suggestions, I appreciate it. Alas, at the least, there are Python implementations of all those between scikit-learn and this project: https://github.com/selective-inference/Python-software https://github.com/selective-inference/Python-software. statsmodels has GLM capabilities, and there are even Python bindings for R's glmnet FORTRAN library. I'm not sure how well those compare to the R implementations, but they look well-built at first glance. Any other ideas out there?
- 10y ago