Robust Regression for arbitrary non-linear functions
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Updated
Jun 16, 2018 - Python
Robust Regression for arbitrary non-linear functions
🎓 Tidy tools for academics
Different type of solvers to solve systems of nonlinear equations
Simple 1d robust regression with huber loss in the case of anomalies / outliers
2019.12.18 개인프로젝트. 통신사 고객들의 이탈 예측
universal rank-order method to analyze noisy data
Fortran 90 library of John Burkardt for regression using least-squares and other criteria, based on Spaeth's codes
Random Sample Consensus (RANSAC) Python Implementation
A Julia package for robust regressions using M-estimators and quantile regressions
Robust regression algorithm that can be used for explaining black box models (R implementation)
Unveiling the Art of Stock Market Prognostication through Regression Algorithms. Delve into our research exploring the power of machine learning in predicting market trends. Discover the secrets behind top regression models like Linear, Robust, Ridge, and Lasso Regression. Unravel the complexities of the market with data-driven precision.
a c++ library with statistical machine learning algorithms for linear and non-linear robust regression that can be used with python.
Robust shape fitting
Weighted BACON algorithms
Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)
Regression Analysis Utility
Robust regression algorithm that can be used for explaining black box models (Python implementation)
Robust locally weighted multiple regression in Python
Flexible Statistics and Data Analysis (FSDA) extends MATLAB for a robust analysis of data sets affected by different sources of heterogeneity. It is open source software licensed under the European Union Public Licence (EUPL). FSDA is a joint project by the University of Parma and the Joint Research Centre of the European Commission.
Scalable and user friendly neural 🧠 forecasting algorithms.
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