  Automated machine learning streamlines the task of finding effective machine
learning pipelines by automating model training, evaluation, and selection.
Traditional evaluation strategies, like cross-validation (CV), generate one
value that averages the accuracy of a pipeline's predictions. This single
value, however, may not fully describe the generalizability of the pipeline.
Here, we present Lexicase-based Validation (lexidate), a method that uses
multiple, independent prediction values for selection. Lexidate splits training
data into a learning set and a selection set. Pipelines are trained on the
learning set and make predictions on the selection set. The predictions are
graded for correctness and used by lexicase selection to identify parent
pipelines. Compared to 10-fold CV, lexicase reduces the training time. We test
the effectiveness of three lexidate configurations within the Tree-based
Pipeline Optimization Tool 2 (TPOT2) package on six OpenML classification
tasks. In one configuration, we detected no difference in the accuracy of the
final model returned from TPOT2 on most tasks compared to 10-fold CV. All
configurations studied here returned similar or less complex final pipelines
compared to 10-fold CV.
