Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantifying contribution and propagation of error from computational
  steps, algorithms and hyperparameter choices in image classification
  pipelines   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 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 components in the pipeline, by computing an error contribution
relative to an agnostic choice of computational steps, algorithms and
hyperparameters. We also propose a methodology to quantify the propagation of
error from individual components of the pipeline with the help of a naive set
of benchmark algorithms not involved in the pipeline. We demonstrate our
methodology on image classification pipelines. The agnostic and naive
methodologies quantify the error contribution and propagation respectively from
the computational steps, algorithms and hyperparameters in the image
classification pipeline. We show that algorithm selection and hyperparameter
optimization methods like grid search, random search and Bayesian optimization
can be used to quantify the error contribution and propagation, and that random
search is able to quantify them 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.

15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
2
The categories of this paper are: math