They're just trying to not lose in the ML space that's all. Wants people to use their cloud server.
Just cause it's specific to microsoftR and the package doesn't mean those algorithms are own by microsoft. You just have to google other packages that have those algorithms. All of them are just statistic... R have tons of statistic package more than Python. I will be highly skeptical if you can't find a LDA, PCA, etc.. in a package somewhere.
The only important part here is the technical knowledge of what it are these algorithms for and when to use them and when not to.
Are you kidding me? You're telling me they're embracing it and not trying to get into the ML share or playing catch up ever since Hadoop came about?
> This seems specific to Microsoft R or can I install the MicrosoftML package in regular R and/or use it under Linux?
You asked a question and I provided an answer. Just cause you didn't like it doesn't mean it's not an alternative to Revolution R. You're just taking it as if it's some attack on you, grow up. I'm stating that if it's not possible then you can always find package for it. They're statistic algorithms that you learn in stat grad courses and R is a statistic language. So you can find it if you can't use Microsoft stuff. So you don't have to worry if it works or not.
I also don't get this fascination on this library. You can just use packages that is agnostic to what R version (R Revolution microsoft or regular R).
They're trying to sell their version of R and hopefully their cloud stuff like Azure.
> The arrogance is visible in the title that doesn't mention it's specific to MS R.
I have no idea what that mean.
All the mx prefix functions are Revolution R only.
Just cause it's specific to microsoftR and the package doesn't mean those algorithms are own by microsoft. You just have to google other packages that have those algorithms. All of them are just statistic... R have tons of statistic package more than Python. I will be highly skeptical if you can't find a LDA, PCA, etc.. in a package somewhere.
The only important part here is the technical knowledge of what it are these algorithms for and when to use them and when not to.