  Machine learning (ML) pipeline composition and optimisation have been studied
to seek multi-stage ML models, i.e. preprocessor-inclusive, that are both valid
and well-performing. These processes typically require the design and traversal
of complex configuration spaces consisting of not just individual ML components
and their hyperparameters, but also higher-level pipeline structures that link
these components together. Optimisation efficiency and resulting ML-model
accuracy both suffer if this pipeline search space is unwieldy and excessively
large; it becomes an appealing notion to avoid costly evaluations of poorly
performing ML components ahead of time. Accordingly, this paper investigates
whether, based on previous experience, a pool of available
classifiers/regressors can be preemptively culled ahead of initiating a
pipeline composition/optimisation process for a new ML problem, i.e. dataset.
The previous experience comes in the form of classifier/regressor accuracy
rankings derived, with loose assumptions, from a substantial but non-exhaustive
number of pipeline evaluations; this meta-knowledge is considered
'opportunistic'. Numerous experiments with the AutoWeka4MCPS package, including
ones leveraging similarities between datasets via the relative landmarking
method, show that, despite its seeming unreliability, opportunistic
meta-knowledge can improve ML outcomes. However, results also indicate that the
culling of classifiers/regressors should not be too severe either. In effect,
it is better to search through a 'top tier' of recommended predictors than to
pin hopes onto one previously supreme performer.
