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
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?
