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
Open a terminal (CMD or PowerShell) in the extraction directory and execute: confusion.exe Use code with caution. Copied to clipboard Confusion-0.5-win.zip
Confusion is a command-line utility designed for data scientists and researchers. Its primary purpose is to take a set of predicted labels and actual (ground truth) labels to produce a detailed breakdown of a model's performance beyond simple accuracy. 0.5 Platform: Windows (Intel/AMD 64-bit) The tool processes classification data to build a
The file is the Windows distribution for Confusion v0.5 , a specialized machine learning tool used to generate and analyze confusion matrices . Indicates "Type II" error (missed detection)
To generate a report using the Windows version, follow these steps:
Detailed performance reports including Sensitivity, Specificity, Precision, and F-score. Core Components & Metrics