Description
When I used data-set in the git-hub example, I get proper predictions. But when I used my data set It does not predict well. (I has a large RMSE).
I used cross validator for ALS (in Spark ML) and here are the best model parameters.
16/03/25 12:03:06 INFO CrossValidator: Average cross-validation metrics: WrappedArray(NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN)
16/03/25 12:03:06 INFO CrossValidator: Best set of parameters:
{
als_c911c0e183a3-alpha: 0.02,
als_c911c0e183a3-rank: 500,
als_c911c0e183a3-regParam: 0.03
}
But when I used movie data set It gives proper values for parameters. as below
16/03/24 14:07:07 INFO CrossValidator: Average cross-validation metrics: WrappedArray(1.9481584447713676, 2.0501457159728944, 2.0600857505406935, 1.9457234533860048, 2.0494498583414282, 2.0595306613827002, 1.9488322049918922, 2.0489573853226797, 2.0584252131752, 1.9464006741621391, 2.048241271354197, 2.057853990227443)
16/03/24 14:07:07 INFO CrossValidator: Best set of parameters:
{
als_31a605e7717b-alpha: 0.02,
als_31a605e7717b-rank: 1,
als_31a605e7717b-regParam: 0.02
}
16/03/24 14:07:07 INFO CrossValidator: Best cross-validation metric: 1.9457234533860048.
Attachments
Issue Links
- duplicates
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SPARK-14489 RegressionEvaluator returns NaN for ALS in Spark ml
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- Resolved
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- links to
