[Paper Review] Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelines

Aritra Chowdhury, Malik Magdon‐Ismail+1 more|arXiv (Cornell University)|Feb 21, 2019

Machine Learning and Data Classification27 references4 citations

TL;DR

This paper proposes a novel framework to quantify error contribution and propagation in image classification pipelines by using agnostic and naive benchmarking methodologies. It demonstrates that random search outperforms Bayesian optimization in accurately estimating error contributions and propagation, enabling data scientists to identify critical pipeline components for targeted improvement.

ABSTRACT

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.

Motivation & Objective

• To develop a method for quantifying the contribution of individual components—computational steps, algorithms, and hyperparameters—to overall pipeline error in image classification.
• To create a framework for analyzing how error propagates through a pipeline, especially from early to later stages.
• To evaluate and compare the effectiveness of optimization strategies (grid search, random search, Bayesian optimization) in estimating error contributions and propagation.
• To provide domain experts with interpretable insights into pipeline performance by identifying the most influential components in terms of error generation and transmission.

Proposed method

• Proposes an 'agnostic methodology' to compute error contribution by replacing each component (step, algorithm, hyperparameter) with a random or baseline choice, measuring the resulting performance degradation.
• Introduces a 'naive methodology' using benchmark algorithms not in the pipeline to model error propagation from each component downstream.
• Employs grid search, random search, and Bayesian optimization as optimization engines to explore the hyperparameter space and estimate error contributions.
• Uses cross-entropy loss and accuracy as evaluation metrics to quantify error at each pipeline stage.
• Compares the error contribution and propagation estimates across optimization methods to assess their reliability and efficiency.
• Applies the framework to image classification pipelines, using real datasets and standard models to validate the approach.

Experimental results

Research questions

• RQ1 How can the contribution of individual components (steps, algorithms, hyperparameters) to overall pipeline error be quantified in a systematic and interpretable way?
• RQ2 To what extent does error propagate from earlier pipeline stages (e.g., feature extraction) to later stages (e.g., classification)?
• RQ3 How do different hyperparameter optimization strategies—grid search, random search, and Bayesian optimization—compare in their ability to accurately estimate error contributions and propagation?
• RQ4 Can random search serve as a reliable proxy for grid search in estimating both error contribution and propagation?
• RQ5 How do error contribution and propagation values correlate across different components in a typical image classification pipeline?

Key findings

• Random search provides more accurate estimates of error contribution than Bayesian optimization, primarily due to its ability to sample a broader region of the hyperparameter space.
• The error propagation analysis shows that the majority of error originates from feature extraction algorithms, followed by feature transformation steps, indicating early-stage components are primary sources of error.
• Error propagation values are proportional to error contribution values, validating the consistency of the proposed model for error flow in pipelines.
• Random search can effectively serve as a proxy for grid search in estimating both error contribution and propagation, offering a faster and equally reliable alternative.
• Bayesian optimization underperforms in error contribution estimation due to its sequential, greedy nature, which limits exploration and leads to biased estimates.
• The agnostic and naive methodologies enable domain experts to interpret pipeline behavior and prioritize components for tuning, improving pipeline efficiency and performance.