Relating instance hardness to classification performance in a dataset: a visual approach.

Area of Science:

• Machine Learning
• Data Visualization
• Computational Statistics

Background:

• Machine learning studies often compare model performance using average statistics, masking individual instance difficulties.
• Existing Instance Space Analysis (ISA) visualizes performance across datasets but not within them.
• Current methods lack fine-grained insights into why algorithms succeed or fail on specific data points.

Purpose of the Study:

• To adapt the Instance Space Analysis (ISA) methodology for a more granular examination of classifier performance.
• To develop a visualization technique that maps individual instance hardness to predictive performance.
• To introduce an open-source Python package, PyHard, for implementing this adapted ISA.

Main Methods:

• Adapted the Instance Space Analysis (ISA) to create a 2-D hardness embedding for individual datasets.
• Extracted instance hardness measures to estimate the difficulty of classifying each observation.
• Developed PyHard, a Python package offering interactive visualization of the adapted ISA.

Main Results:

• The adapted ISA provides a detailed visualization of data instances based on their difficulty.
• Demonstrated the ability to analyze relationships between instance hardness and classifier predictive performance.
• Case studies illustrated insights into data quality issues like noise and bias.

Conclusions:

• The adapted ISA offers a powerful tool for deeper analysis of machine learning model behavior on specific datasets.
• PyHard facilitates the identification of challenging instances and potential data quality problems.
• This approach enhances understanding of algorithm performance beyond simple average metrics.