domain-adaptationmachine learning
I refer to the paper by called Frustratingly Easy Domain Adaptation (http://www.umiacs.umd.edu/~hal/docs/daume07easyadapt.pdf) where the feature space of both the source and target data are augmented and used as input to a standard learning algorithm. The code is just 10 lines but in perl. I want to port to python but don't understand what the input data looks like that must be processed.
http://hal3.name/easyadapt.pl.gz
Thanks
It seems that there are some disagreement between researchers on what the difference between 'transfer learning' and 'domain adaptation' is.
From {0}:
The notion of domain adaptation is closely related to transfer learning. Transfer learning is a general term that refers to a class of machine learning problems that involve different tasks or domains. In the literature, there isn't yet a standard definition of transfer learning. In some papers it's interchangeable with domain adaptation.
From {1}:
References:
{0} Li, Qi. "Literature survey: domain adaptation algorithms for natural language processing." Department of Computer Science The Graduate Center, The City University of New York (2012): 8-10. https://scholar.google.com/scholar?cluster=2828982016930721315&hl=en&as_sdt=0,22 ; https://pdfs.semanticscholar.org/532e/3d5b1b5807771b77cac60fe8594b506fcff9.pdf ; http://nlp.cs.rpi.edu/paper/qisurvey.pdf (mirror)
{1} Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22, no. 10 (2010): 1345-1359. https://scholar.google.com/scholar?cluster=17771403852323259019&hl=en&as_sdt=0,22 ; http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.158.4126&rep=rep1&type=pdf (mirror) (2.6k citations)
This will probably be closed quickly as opinion-based, but here is a point you may want to consider.
200 features is a lot, and 30k rows is less than it sounds like. A "fishing expedition" to find relevant features is quite likely to overfit and select spurious features. The danger is that when you go to your domain experts with these features you "found" to be relevant, they may not push back. Instead, it's a very common human reaction to start telling stories about how these features are indeed useful, because we humans are very good at explaining stuff, even stuff that is simply noise.
Talking to your domain experts first will not completely avoid this problem, but it may reduce the number of wild goose chases.
You may be interested in my answer to "How to know that your machine learning problem is hopeless?".
Best Answer
The input data are the files that you will read (those files will contain the lines of text).
Please refer to this link for more details on the code: