  Data science relies on pipelines that are organized in the form of
interdependent computational steps. Each step consists of various candidate
algorithms that maybe used for performing a particular function. Each algorithm
consists of several hyperparameters. Algorithms and hyperparameters must be
optimized as a whole to produce the best performance. Typical machine learning
pipelines typically consist of complex algorithms in each of the steps. Not
only is the selection process combinatorial, but it is also important to
interpret and understand the pipelines. We propose a method to quantify the
importance of different layers in the pipeline, by computing an error
contribution relative to an agnostic choice of algorithms in that layer. We
demonstrate our methodology on image classification pipelines. The agnostic
methodology quantifies the error contributions from the computational steps,
algorithms and hyperparameters in the image classification pipeline. We show
that algorithm selection and hyper-parameter optimization methods can be used
to quantify the error contribution and that random search is able to quantify
the contribution more accurately than Bayesian optimization. This methodology
can be used by domain experts to understand machine learning and data analysis
pipelines in terms of their individual components, which can help in
prioritizing different components of the pipeline.
