It's something like the average error in rating, but a bit different
-- it's the square root of average squared error. But if you think of
the ratings as 'stars' you could kind of think of 0.86 as 'generally
off by 0.86' stars and that would be somewhat right.
Whether that's good depends on what t
Yep, much better with 0.1.
"The best model was trained with rank = 12 and lambda = 0.1, and numIter =
20, and its RMSE on the test set is 0.869092" (Spark 1.3.0)
Question : What is the intuition behind RSME of 0.86 vs 1.3 ? I know the
smaller the better. But is it that better ? And what is a good
I am using Spark 1.2.1.
Thank you Krishna, I am getting almost the same results as you so it must
be an error in the tutorial. Xiangrui, I made some additional tests with
lambda to 0.1 and I am getting a much better rmse:
RMSE (validation) = 0.868981 for the model trained with rank = 8, lambda =
Try to set lambda to 0.1. -Xiangrui
On Mon, Feb 23, 2015 at 3:06 PM, Krishna Sankar wrote:
> The RSME varies a little bit between the versions.
> Partitioned the training,validation,test set like so:
>
> training = ratings_rdd_01.filter(lambda x: (x[3] % 10) < 6)
> validation = ratings_rdd_01.fil
1. The RSME varies a little bit between the versions.
2. Partitioned the training,validation,test set like so:
- training = ratings_rdd_01.filter(lambda x: (x[3] % 10) < 6)
- validation = ratings_rdd_01.filter(lambda x: (x[3] % 10) >= 6 and
(x[3] % 10) < 8)
- test = ratin
Which Spark version did you use? Btw, there are three datasets from
MovieLens. The tutorial used the medium one (1 million). -Xiangrui
On Mon, Feb 23, 2015 at 8:36 AM, poiuytrez wrote:
> What do you mean?
>
>
>
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> View this message in context:
> http://apache-spark-user-list.1001560.n3.nabbl
What do you mean?
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