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>
