yuhao yang created SPARK-18755:
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Summary: Add Randomized Grid Search to Spark ML
Key: SPARK-18755
URL: https://issues.apache.org/jira/browse/SPARK-18755
Project: Spark
Issue Type: Improvement
Components: ML
Reporter: yuhao yang
Randomized Grid Search implements a randomized search over parameters, where
each setting is sampled from a distribution over possible parameter values.
This has two main benefits over an exhaustive search:
1. A budget can be chosen independent of the number of parameters and possible
values.
2. Adding parameters that do not influence the performance does not decrease
efficiency.
Randomized Grid search usually gives similar result as exhaustive search, while
the run time for randomized search is drastically lower.
For more background, please refer to:
sklearn: http://scikit-learn.org/stable/modules/grid_search.html
http://blog.kaggle.com/2015/07/16/scikit-learn-video-8-efficiently-searching-for-optimal-tuning-parameters/
http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
https://www.r-bloggers.com/hyperparameter-optimization-in-h2o-grid-search-random-search-and-the-future/.
There're two ways to implement this in Spark as I see:
1. Add searchRatio to ParamGridBuilder and conduct sampling directly during
build.
2. Add trait RadomizedSearch and create new class RandomizedCrossValidator and
RandomizedTrainValiationSplit.
I'd prefer option 1 as it's much simpler and straightforward.
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