There are 3 repositories under error-analysis topic.
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.
Analysis of digital elevation models (DEMs)
[ECCV'24] On the Error Analysis of 3D Gaussian Splatting and an Optimal Projection Strategy
[NeurIPS 2025 (D&B)] Rethinking Evaluation of Infrared Small Target Detection
Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
Model verification, validation, and error analysis
OlliePy is a python package which can help data scientists in exploring their data and evaluating and analysing their machine learning experiments by utilising the power and structure of modern web applications. The data scientist only needs to provide the data and any required information and OlliePy will generate the rest.
Process global-scale satellite and airborne elevation data into time series of glacier mass change: Hugonnet et al. (2021).
Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice
A tool for classifying mistakes in the output of parsers
A tool for classifying errors in coreference resolution
A package for handling numeric quantities with asymmetric uncertainties.
Tests the Black-Scholes model's performance on forecasting option call prices of a selected option chain dataset. Discusses factors such as volatility and time to expiration that affect the estimations of call option prices and how this occurs within the dynamics of the model.
Process case studies on DEM uncertainty analysis at the Mont-Blanc massif and Northern Patagonian Icefield: Hugonnet et al. (2022).
Automatically export Windows event logs to CSV
CECS (C/C++ Error Control System) is a pure C libary, with C++ support, for textual informative error control. Errors, Warnings or Info that is recording during runtime can be traced back to the file and line where the error occured.
Collect and Submit PR Feedback using Gitea Actions Annotations
Nobunaga: Object Detection Analyzer
Pyshifts: A Pymol Plugin for Chemical Shift-Based Analysis of Biomolecular Ensembles
Source code and the details of the results in the paper "Named entity recognition in Turkish: A comparative study with detailed error analysis".
A VSCode extension that leverages Gemini AI to analyze and explain errors in Jupyter notebooks, helping beginners debug with ease.
Generate error bars and perform binning analysis using jackknife or bootstrap resampling. Calculate average and error in quantum Monte Carlo data (or other data) and on functions of averages (such as fluctuations, skew, and kurtosis).
Built an error prediction system using LSTM and integrated the model in Flask to make it a web application. Collected logs were trained using the model, and tested on the log data without error.
Toolkit for studying numerical analysis and floating point algebra round-off
Code for modeling and data analysis of PHYS128 AL@UC Santa Barbara. For Demonstration.
Machine learning techniques, such as Linear Regression, Logistic Regression, Neural Networks (feedforward propagation, backpropagation algorithms), Diagnosing Bias/Variance, Evaluating a Hypothesis, Learning Curves, Error Analysis, Support Vector Machines, K-Means Clustering, PCA, Anomaly Detection System, and Recommender System.
Numerical methods are a collection of techniques used to find approximate solutions to mathematical problems that cannot be solved exactly. These methods are essential for tackling complex equations and models in various fields such as engineering, physics, and finance.
Analysis of the most famous historical catastrophic medical radiotherapy device including it's catastrophic software anti-patterns.
This repo helps you to implement sentiment analysis on any text data using two machine learning algorithms Logistic Regression and Naive Bayes
Post-mortem debugging tool for Python that provides direct access to variables and frames after exceptions occur. Rich tracebacks, frame inspection, and context execution without separate interactive shells.
Research on numerical investigations of nonlinear first-order differential equations through unity approximations in nonstandard finite difference schemes