Is it fair to assume this is what I need? https://github.com/ispras/pu4spark
On Mon, Jan 15, 2018 1:55 PM, Georg Heiler georg.kf.hei...@gmail.com wrote: As far as I know spark does not implement such algorithms. In case the dataset is small http://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html might be of interest to you. Jörn Franke <jornfra...@gmail.com> schrieb am Mo., 15. Jan. 2018 um 20:04 Uhr: I think you look more for algorithms for unsupervised learning, eg clustering. Depending on the characteristics different clusters might be created , eg donor or non-donor. Most likely you may find also more clusters (eg would donate but has a disease preventing it or too old). You can verify which clusters make sense for your approach so I recommend not only try two clusters but multiple and see which number is more statistically significant . On 15. Jan 2018, at 19:21, Matt Hicks <m...@outr.com> wrote: I'm attempting to create a training classification, but only have positive information. Specifically in this case it is a donor list of users, but I want to use it as training in order to determine classification for new contacts to give probabilities that they will donate. Any insights or links are appreciated. I've gone through the documentation but have been unable to find any references to how I might do this. Thanks --- Matt Hicks Chief Technology Officer 405.283.6887 | http://outr.com <logo 2 small.png>
