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- parallel evaluation with joblib (n_jobs)
- implement genetic algorithm to find pipelines
- copy parameters from tpot
- add rules to prevent stupid things (PolynomialFeatures with many columns)
- distribute genetic algorithms with dask
- test joblib distributed backend with dask (nothing to do, just test)
- fine-grained distribution with dask computation graph:
- trivial for prediction
- for fit, each step returns a cross-validated estimate and a fitted model. The fitted model is not used before the final step.
- it is possible to implement cross-validation with a factor 2 improvement when the cross-val and the training folds match
- handle timeouts: https://github.com/dask/distributed/issues/391, https://github.com/dask/dask/issues/1183
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