aset by cluster id and aggregate over the original 5
features. E.g., get the mean for numerical data or the value that occurs the
most for categorical data.
The exact aggregation is use-case dependent.
I hope this helps,Christoph
Am 01.03.2018 21:40 schrieb "Matt Hicks" :
Thanks for th
I'm using K Means clustering for a project right now, and it's working very
well. However, I'd like to determine from the clusters what information
distinctions define each cluster so I can explain the "reasons" data fits into a
specific cluster.
Is there a proper way to do this in Spark ML?
nfo] ([-1.0,1.5,1.3], 1.0) -> prob=[0.0,1.0], prediction=1.0[info]
([3.0,2.0,-0.1], 0.0) -> prob=[0.0,1.0], prediction=1.0[info] ([0.0,2.2,-1.5],
1.0) -> prob=[0.0,1.0], prediction=1.0
On Tue, Jan 16, 2018 8:51 AM, Matt Hicks m...@outr.com wrote:
Hi Hari, I'm not sure I u
ng to
the donor class. And it'll be same as what's the probability the a person will
become donor.
Best Regards,Hari
On 15 Jan 2018 11:51 p.m., "Matt Hicks" wrote:
I'm attempting to create a training classification, but only have positive
information. Specifically in this
ights 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
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