Hyperparameter Optimization & Custom Evaluation
Two main features this release:
Hyperparameter Optimization
Hyperparameters can be optimized on the best found pipeline via the skplumber.SKPlumber.crank(..., tune=True)
API or the on any single pipeline using the skplumber.tuners.ga.ga_tune
method. This is accomplished via the flexga
package and hyperparameter annotations which have been added to all machine learning primitives.
Custom Evaluation
Previously, skplumber.SKPlumber.crank
could only do k-fold cross validation. Now, by passing in a custom evaluator e.g. skplumber.SKPlumber.crank(..., evaluator=my_evaluator)
, any other pipeline evaluation method can be used. skplumber
provides evaluators for k-fold cross validation, simple train/test splitting, and down-sampled train/test splitting.