2 Repos
Tools for generating curves and graphs that visualize the trade-off between precision and recall.
Distinct from Recall Optimization: Existing candidates focus on search engine recall or memory recall, not ML performance curve graphing.
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Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The
Ships precision, recall, F-measure, and support calculations for each class in classification results.
This project is an object detection evaluation library and benchmarking tool designed to calculate precision, recall, and average precision for computer vision models. It provides a suite of utilities for parsing bounding box coordinates from text files and calculating spatial overlap to determine detection accuracy. The toolkit features a command line interface for comparing ground truth files against model predictions. It includes a precision-recall curve generator to visualize the relationship between precision and recall across different confidence thresholds and an intersection over unio
Generates curves and graphs to analyze the trade off between detection sensitivity and accuracy across classes.