Github user yanboliang commented on the issue:
https://github.com/apache/spark/pull/16011
@MLnick Yeah, I think this is the most common case that copying Params from
estimators to models. However, I also found some algorithms do not comply this
rule, such as ```ALS``` which has ```ALSParams``` and ```ALSModelParams``` for
estimator and model separately.
I think we can set params to models not via estimator, for example:
```
val discretizer = new QuantileDiscretizer()
val pipeline = new Pipeline().setStages(Array(discretizer))
val model = pipeline.fit(df)
model.stages(0).asInstanceOf[Bucketizer].setHandleInvalid("skip")
```
I know this way is a little tricky, a better way may be we can have
```QuantileDiscretizerModel``` which is produced by ```QuantileDiscretizer```.
Think more about it, ```Bucketizer``` is a separate transformer which
mainly has two params(```splits``` and ```handleInvalid```) can be set. Users
can provides candidates for these two params when doing cross validation to
select the best model. But if we constrict it must be produced by
```QuantileDiscretizer```, the ```splits``` would be a member variable of the
model rather than a param. From this perspective, it's more make sense to see
```Bucketizer``` as a separate transformer.
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