Major Description
The classic cross-validation requires all inner classifiers iterate to a fixed number of iterations, or until convergence states. It is costly especially in the massive data scenario. According to the paper Non-stochastic Best Arm Identification and Hyperparameter Optimization (http://arxiv.org/pdf/1502.07943v1.pdf), we can see a promising way to reduce the amount of total iterations of cross-validation with multi-armed bandit search.
The multi-armed bandit search for cross-validation (bandit search for short) requires warm-start of ml algorithms, and fine-grained control of the inner behavior of the corss validator.
Since there are bunch of algorithms of bandit search to find the best parameter set, we intent to provide only a few of them in the beginning to reduce the test/perf-test work and make it more stable.
Attachments
Issue Links
- is related to
-
SPARK-11136 Warm-start support for ML estimator
-
- Resolved
-
- links to
