Confusion-0.5-win.zip Apr 2026

For multi-class datasets, the report provides both macro-averaged (equal weight to each class) and micro-averaged (equal weight to each instance) scores.

By highlighting high values in non-diagonal cells, the tool helps identify which specific classes the model is frequently "confusing" with others. Confusion-0.5-win.zip

Below is a detailed report on the software, its core functionality, and how to utilize it for performance evaluation in classification tasks. To generate a report using the Windows version,

To generate a report using the Windows version, follow these steps: Measures successful identification

The program will print a formatted matrix to the console along with a summary report for each class. Advanced Analysis Features

The tool processes classification data to build a matrix where rows represent actual classes and columns represent predicted classes. Definition Importance Correctly predicted positive instances. Measures successful identification. False Positives (FP) Incorrectly predicted positive instances. Indicates "Type I" error (false alarm). False Negatives (FN) Missed positive instances. Indicates "Type II" error (missed detection). Precision Shows how reliable the positive predictions are. Recall (Sensitivity) Shows how many actual positives the model found. Usage Instructions

The file is the Windows distribution for Confusion v0.5 , a specialized machine learning tool used to generate and analyze confusion matrices .