Justin Yip created SPARK-8298:
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Summary: Sliding Window CrossValidator
Key: SPARK-8298
URL: https://issues.apache.org/jira/browse/SPARK-8298
Project: Spark
Issue Type: Improvement
Components: ML
Reporter: Justin Yip
CrossValidator only supports k-folds. It cannot prevent the validation data
from look-ahead bias. I would like to contribute a sliding-window based
CrossValidator. The sliding window guarantees a clear cutoff time between the
training and validation data, to prevent look-ahead bias.
Three parameters are used to govern the generation process.
1. numFold - Int
2. firstCutoffTime - Timestamp, the cutoff time of the training data for the
first (training, validation) data pair
3. validationWindowSize - Long, millis of the validation data set duration.
Need to decide:
Whether to make the current CrossValidator more generic or implement a new
SlidingWindowCrossValidator.
- Most of the logic are identical between CrossValidator and
SlidingWindowValidator, except for the part where the training-validation data
pairs is generated. More, if we introduce other kinds of data splitting
methods, there will be lots of code redundancy if we use multiple classes.
- However, I also foresee that things will get messy to support too many
splitting methods with one CrossValidator class.
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