Top 20 NuGet regression Packages

Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .Net Framework 4.0 or higher and .Net Standard 1.3 or higher, on Windows, Linux and Mac.
Provides learning algorithms and models for DecisionTree regression and classification.
F# Modules for Math.NET Numerics, the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .Net Framework 4.5 or higher and .Net Standard 1.6 or higher, on Windows, Linux and Mac.
Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .Net Framework 4.0 or higher and .Net Standard 1.3 or higher, on Windows, Linux and Mac. This package contain...
Provides learning algorithms and models for RandomForest and ExtraTrees regression and classification.
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solut...
Provides learning algorithms and models for XGBoost regression and classification.
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solut...
Provides ensemble learning for regression and classification.
Provides learning algorithms and models for neural net regression and classification.
The Extreme Optimization Numerical Libraries for .NET are a set of libraries for numerical computing and data analysis. This is the main package that contains all the core functionality. For optimal performance, we strongly recommend also referencing one of the native packages based on Intel's Mat...
Provides learning algorithms and models for GradientBoost regression and classification.
Provides classification, regression, impurity and ranking metrics.
Provides learning algorithms and models for AdaBoost regression and classification.
Contains a matrix extension library, along with a suite of numerical matrix decomposition methods, numerical optimization algorithms for constrained and unconstrained problems, special functions and other tools for scientific applications. This package is part of the Accord.NET Framework.
Contains probability distributions, statistical models and methods such as Linear and Logistic regression, Hidden Markov Models, (Hidden) Conditional Random Fields, Principal Component Analysis, Partial Least Squares, Discriminant Analysis, Kernel methods and functions and many other related techniq...
F# Modules for Math.NET Numerics, the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .Net Framework 4.5 or higher and .Net Standard 1.6 or higher, on Windows, Linux and Mac. This pa...
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solut...
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solut...
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solut...