It the first time to deal with weka and ML. it's hard for me to read (detailed accuracy by class). Can anyone help me, please?
If you have any link or resources may help me.
I did an annotation of J48 output when I taught a predictive analytics class a couple of years ago. This will answer many of your questions (probably not all!).
https://www.dropbox.com/s/ssst947s2nntqyu/Annotated%20J48%20output.docx?dl=0
Related
I came across this Stackoverflow question where the fellow user was trying to implement a LRU cache with must join. As I am a beginner in C++ I am struggling to implement and check how a may-join might work for a LRU cache. The resources for learning about this topic is very limited and I would be grateful if someone could help me out with this as it's hard to learn and code whilst having a full time job. Any help would be appreciated.
I understood the must join part but currently struggling with may join for over 2 months.
The code is present in the link above and I haven't copied the code here as it might be a plagiarism from my part. Kindly help with the code or refer to some good materials. Thank you
This link might help for a deep understanding. https://www.rw.cdl.uni-saarland.de/people/reineke/private/talks/cachesInWCETAnalysis.pdf
I know a little bit of ML, and want to implement a learning system by myself,but do not know how to do.Any one can give me a demo or use other method to compare faces?
Here is a related post: https://stackoverflow.com/questions/14079794/how-to-recognize-face-by-geometric-feature-such-as-eyes-nose-mouth.
One can not reasonably answer this question bassed on the above information because of sheer vastness of the subject.
For the start you should know that these problems are usually solved using Machine Learning techniques like Neural Networks. You said you know a bit about ML but as IMHO you might want to read more or take an online Course on Machine learning.
There are some good Courses on Coursera.org one that I like is Machine Learning by Andrew Ng.
These Methods are also described in above mentioned course and there are some good assignments too, which will help you to get the detailled idea behind machine learning.
After finding this enormously helpful guide in R, it got me wondering how I might do something similar in Incanter. Being relatively new to Incanter, it would be lovely if someone could reproduce this answer.
In addition to illustrating a nested model, the discussion on that answer also included some good discussion of how to iteratively generate a list of un-nested models. I'd be curious as to what is the most idiomatic way of doing that in Clojure/Incanter is.
I'm in need of adding a plans and pricing schema to my website to charge a user for the use of certain feature. In this case, the User adds Notices to Points.
A User should not be able to add more Notices than his' Plan allows. Stuff like that.
A quick google round gave me nothing to look around (django pricing and plans are terrible search terms) so i was thinking in rolling my own solution but may be you know of something of interest.
I'm not very sold to the way the pricing schema would work or how to handle the pricing, but that's not in the scope of the question.
We are actively developing a possible solution:
https://github.com/cypreess/django-plans
both with the https://github.com/cypreess/django-getpaid (which is a payment broker) they can solve all kind of problems with: plans, pricing, quotas (limits), invoicing, taxation, payments.
getpaid has a stable version, plans are still in development, but it is 100% usable at this point (I am using it personally).
Both have a pretty good documentation, that could be found on read the docs.
I hope that would help you.
You might try looking around for quota systems. Your needs will probably be pretty tightly integrated with your application, so implementing a solution from the ground up is likely to be the right way to do it.
I see this which probably isn't quite what you need, but might give you some ideas: https://github.com/mpasternacki/django-quotas
This might not be a decent answer for your question. But I dug a lot into this without finding a decent solution. I've stumbled upon django-plans but not even the examples work anymore. And their last commit was a year ago. But they do have issues that are months old.
I think since it's a very specific thing to countries' laws and eventually you will get to a point where the lib doesn't support what you wanna do. That said, I would develop my own. That's what I am doing now.
recently I attended a talk on data-mining,and I missed some points by the lecturer,which is about a technique used on data-mining,and which is especially useful for blog webpages.
I think I sort of remembered the term is named as "td/tdf" something,but really not sure.
I googled for this for a while,still have no result.
It would be great if you could point out what this technique is,since I want to look further into this topic.
Thanks a lot.
I think maybe you mean temporal difference learning.
Edit
As mentioned in the comments, actual answer is Term Frequency / Inverse Document Frequency.