Confusion Matrix for Dynamic-Static Classification with 93.7% Accuracy

93.7% overall accuracy is reported for a dynamic-static classification task. The dataset is a confusion matrix, likely containing counts of true vs. predicted labels, shared by author Hongmin Wang on figshare. It was last updated on June 2, 2026.

Use Cases

• Calculate precision, recall, and F1-score based on the confusion matrix counts.
• Analyze model error patterns for dynamic versus static classification.
• Benchmark new classification models against the reported 93.7% accuracy.
• Visualize classification performance using the confusion matrix data.

Strengths

• Reported overall accuracy of 93.7% provides a clear performance benchmark.
• Data is shared under a permissive CC-BY-4.0 license.
• Small file size (5.5 KB) allows for quick download and inspection.

Limitations

• The dataset is extremely small (5.5 KB), indicating very limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Freshness

Last updated 2026-06-02 17:35:59; freshness should be verified.