Several spark.ml models now have summaries containing evaluation metrics and training info:
These summaries have unfortunately been added in an inconsistent way. I propose to reorganize them to have:
- For each model, 1 summary (without training info) and 1 training summary (with info from training). The non-training summary can be produced for a new dataset via evaluate.
- A summary should not store the model itself as a public field.
- A summary should provide a transient reference to the dataset used to produce the summary.
This task will involve reorganizing the GLM summary (which lacks a training/non-training distinction) and deprecating the model method in the LinearRegressionSummary.
|Update GeneralizedLinearRegressionSummary API||Resolved|
|Update LinearRegression, LogisticRegression summary APIs||Resolved|
|Update LinearRegression, LogisticRegression summary internals to handle model copy||Resolved||Unassigned|