Random Forest Confusion Matrix for a 20,000-Row Test Set

A single confusion matrix evaluating a Random Forest model's performance on a test set of 20,000 instances. The 5.5 KB Excel file was published by Lalit Narayan Mishra on figshare in May 2026. Its specific application domain and the underlying classification labels are not detailed in the available metadata.

Use Cases

• Calculate standard classification metrics like accuracy, precision, and recall based on the confusion matrix counts.
• Analyze the types of errors (false positives vs. false negatives) made by the Random Forest model.
• Use as a teaching example for illustrating model performance evaluation in machine learning courses.
• Benchmark the performance of a new model against the provided Random Forest results.

Strengths

• The test set size of 20,000 instances is explicitly stated, providing a clear scale for evaluation.
• The data is shared under a permissive CC-BY-4.0 license, allowing for broad reuse with attribution.
• The file is very small (5.5 KB), ensuring quick download and easy inspection.

Limitations

• The dataset's description is minimal, lacking context on the classification task, feature space, or data source.
• Row and column counts for the matrix itself are unknown, making its exact structure ambiguous.
• The underlying data used to generate the matrix is not included, preventing re-analysis or model retraining.

Provenance

Source

figshare, author Lalit Narayan Mishra.

Collection Method

Likely generated from the output of a Random Forest classifier's predictions on a held-out test set.

Freshness

Last updated 2026-05-27 17:39:29; freshness should be verified.