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| META TOPICPARENT |
name="MLRoad-map" |
Overview |
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- Flag boguses with light curve observations greater than 2.
- Catch variable stars with isdiffpos=True and false in the light curve
- Implement proper grid search over RF parameter space
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- finish the work with the alternate test set, and getting stats on that.
- make sure imputation is happening correctly
- get Tomas's other plots integrated
- KL divergence code, should be updated.
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- Pipeline Analysis to automate:
- Score improvement on known false positive, negatives (Ragnhild's List)
- KL divergence between training set, test set features (to find major divergence)
- Plot feature distributions on reals vs. boguses (Tiara's code)
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- What do low RB reals look like, what do high RB bogus look like?
- Unlabeled Data
- Score bias per features
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- Classifier error analysis: What do low RB reals look like, what do high RB bogus look like?
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- Report feature importance by correlated feature groups
Data Collection
- Automated Data Contamination [ Charlotte ]
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