Hello everyone,
I have a data frame which has two columns: ids and features
each cell in feature column is an array of Vectors.dense type.
like:
[(DenseVector([0.5692]),), (DenseVector([0.5086]),)]
I need to train a new model for every single row of my data frame. How can
I do it?
On Sat, Nov 18, 2017 at 9:53 AM, Stephen Boesch <java...@gmail.com> wrote:
> In BinaryLogisticRegressionSummary there are @Since("1.5.0") tags on a
> number of comments identical to the following:
>
> * @note This ignores instance weights (setting all to 1.0) from
> `LogisticRegression.weightCol`.
> * This will change in later Spark versions.
>
>
> Are there any plans to address this? Our team is using instance weights
> with sklearn LogisticRegression - and this limitation will complicate a
> potential migration.
>
> https://github.com/apache/spark/blob/master/mllib/src/
> main/scala/org/apache/spark/ml/classification/
> LogisticRegression.scala#L1543
>
>
>
