Automatic Feature Engineering¶
Driverless AI performs automatic feature engineering as part of an experiment’s model building process. New features are created by doing transformations and/or interactions on the dataset columns. Feature creation and selection is evolutionary (based on variable importance of previous iteration) in nature and uses genetic algorithm to find the best set of feature transformations and model parameters for an experiment/dataset.
The details about features created (transformations applied) and used by the experiment can be obtained from the Autodoc report of an experiment.
The feature engineering effort and evolution can be controlled from the Features Settings on the expert pannel of an experiment.
User can also upload their own custom transformers to be included in addition to the Driverless AI built in transformers. Some opensource custom transormers can be obtained from Driverless AI opensource custom recipes.
The inclusion of the transformers can be controlled from the Recipe tab of the expert pannel of an experiment.
Preprocessing transformers can be used to control the features fed into the evolutionary transformer layer.
User can obtain the dataset with (engineered) features by clicking Transform Dataset from Model Actions of a finished experiment. This provides the pipeline of the best individual model of the experiemnt.