substantial
computational performance benefits; however, a degradation in model accuracy
is a potential trade-off.
--
View this message in context:
http://lucene.472066.n3.nabble.com/Logistic-Regression-poor-results-on-small-data-set-tp3149694p3150228.html
Sent from the Mahout User List mailing list
:
http://lucene.472066.n3.nabble.com/Logistic-Regression-poor-results-on-small-data-set-tp3149694p3149694.html
Sent from the Mahout User List mailing list archive at Nabble.com.
On Thu, Jul 7, 2011 at 2:20 PM, hakeem t...@indeed.com wrote:
Because I have so few documents, I run the set of documents through train()
in epochs -- up to 1000 times, shuffling the order of the documents on each
epoch.
Fair.
My questions:
1) Are these results surprising to you? Or,
If you keep the probes at 2, you should have better results with sparse
features and a large dimensionality reduction.
On Thu, Jul 7, 2011 at 5:58 PM, hakeem t...@indeed.com wrote:
I increased the vector size substantially and reduced the number of probes
to 1. With the collisions eliminated,