ConfusionFlow: A Model-Agnostic Visualization for Temporal Analysis of Classifier Confusion

Area of Science:

• Machine Learning
• Data Visualization
• Supervised Learning

Background:

• Classifiers are essential supervised machine learning algorithms, but selecting and debugging them is challenging.
• Current performance analysis often relies on single metrics like accuracy, lacking detailed insights into class errors.
• Existing visualization tools like confusion matrices are not optimized for temporal or comparative analysis.

Purpose of the Study:

• To introduce ConfusionFlow, an interactive visualization tool for analyzing and comparing classifier performance.
• To enable combined temporal and comparative analysis of class-level information for machine learning models.
• To address the limitations of existing methods in evaluating classifier learning behavior over time.

Main Methods:

• Developed ConfusionFlow, an interactive and comparative visualization tool.
• Integrated confusion matrices with temporal performance visualization.
• Demonstrated model-agnostic capabilities for comparing diverse models and datasets.

Main Results:

• ConfusionFlow facilitates detailed assessment of classifier performance, including class errors and learning dynamics.
• The tool effectively supports comparative analysis across different model types, architectures, and datasets.
• Case studies in active learning and neural network pruning highlight ConfusionFlow's utility and scalability.

Conclusions:

• ConfusionFlow offers a novel approach to classifier performance analysis, enhancing model selection and debugging.
• The tool provides valuable insights beyond traditional single-number metrics.
• ConfusionFlow is a scalable and versatile solution for machine learning practitioners.