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Posted to issues@madlib.apache.org by "Frank McQuillan (JIRA)" <ji...@apache.org> on 2016/04/13 22:46:25 UTC
[jira] [Updated] (MADLIB-640) SVM Novelty: Result is incorrect
class which is "OUT"
[ https://issues.apache.org/jira/browse/MADLIB-640?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Frank McQuillan updated MADLIB-640:
-----------------------------------
Assignee: Nandish Jayaram (was: Rahul Iyer)
> SVM Novelty: Result is incorrect class which is "OUT"
> -----------------------------------------------------
>
> Key: MADLIB-640
> URL: https://issues.apache.org/jira/browse/MADLIB-640
> Project: Apache MADlib
> Issue Type: Bug
> Reporter: Jiali Yao
> Assignee: Nandish Jayaram
> Labels: severity_set
> Fix For: v1.9.1
>
>
> We use data sets which class = 1 as "IN" and verify result in two side:
> 1. All class = 1 items should be as marked as "IN"
> 2. All class = -1 items should be as marked as "OUT"
> But in MADlib result, we can see that all items are marked as "IN".
> Below is one example:
> {code}
> -- method: svm_nd_dot_ds_0_0_svm_novelty_detection_0
> SELECT madlib.svm_novelty_detection
> ( 'madlibtestdata.svm_a9a_in'::text --input_table
> , 'madlibtestresult.nd_model_table'::text --model_table
> , 'true'::boolean --parallel
> , 'madlib.svm_dot'::text --kernel_func
> , 'false'::boolean --verbose
> , '0.01'::float8 --eta
> , '0.005'::float8 --nu
> ) AS q;
> -- All class should be return -1
> -- method: svm_nd_dot_ds_0_0_svm_nd_predict_score_3
> SELECT madlibtestdata.svm_nd_predict_score
> ( 'madlibtestresult.nd_model_table'::text --model_table
> , 'madlibtestdata.svm_a9a_out'::text --input_table
> , 'true'::boolean --parallel
> ) AS score;
> {code}
> Currently for this query all class return 1.
> Data sets name TrainSize TestSize Attributes Rate(1:-1) Missing Source URL
> a9a 32561 16281 123 11687:37155 N http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#a9a
> We have tested multiple datasets,
> Parallel = true:
> 5/5 dataset on Gaussian return score = 0
> 5/5 dataset on Polyminal return score = 0
> 4/5 dataset on dot return score = 0
> Parallel = false:
> 5/5 dataset on Gaussian return score = 0
> 3/5 dataset on Polyminal return score = 0
> 1/5 dataset on dot return score = 0
> When score > 0, we have one data set has better result than libsvm.
> When in parallel = true, we use most vote model as select prediction value
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