  The evaluation of machine learning (ML) pipelines is essential during
automatic ML pipeline composition and optimisation. The previous methods such
as Bayesian-based and genetic-based optimisation, which are implemented in
Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them.
Therefore, the pipeline composition and optimisation of these methods requires
a tremendous amount of time that prevents them from exploring complex pipelines
to find better predictive models. To further explore this research challenge,
we have conducted experiments showing that many of the generated pipelines are
invalid, and it is unnecessary to execute them to find out whether they are
good pipelines. To address this issue, we propose a novel method to evaluate
the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR
enables to accelerate automatic ML pipeline composition and optimisation by
quickly ignoring invalid pipelines. Our experiments show that the AVATAR is
more efficient in evaluating complex pipelines in comparison with the
traditional evaluation approaches requiring their execution.
