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https://issues.apache.org/jira/browse/SPARK-2495?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14064251#comment-14064251
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Xiangrui Meng commented on SPARK-2495:
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They were not by mistake but we were not sure about whether the constructor
arguments were final. For linear methods, we can remove the `private[mllib]`
tag so user can construct a model directly. For naive Bayes, I'm thinking about
making the conditional probability matrix a Matrix type instead of
Array[Array[Double]]. But we can do this step by step, starting from linear
methods. Please submit a PR and ping me. Thanks!
> Ability to re-create ML models
> ------------------------------
>
> Key: SPARK-2495
> URL: https://issues.apache.org/jira/browse/SPARK-2495
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.0.1
> Reporter: Alexander Albul
>
> Hi everyone.
> Previously (prior to Spark 1.0) we was working with MLib like this:
> 1) Calculate model (costly operation)
> 2) Take model and collect it's fields like weights, intercept e.t.c.
> 3) Store model somewhere in our format
> 4) Do predictions by loading model attributes, creating new model and
> predicting using it.
> Now i see that model's constructors have *private* modifier and cannot be
> created from outside.
> If you want to hide implementation details and keep this constructor as
> "developer api", why not to create at least method, which will take weights,
> intercept (for example) an materialize that model?
> A good example of model that i am talking about is: *LinearRegressionModel*
> I know that *LinearRegressionWithSGD* class have *createModel* method but the
> problem is that it have *protected* modifier as well.
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