76 packages returned for Tags:"regression"

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.

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... More information

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... More information

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.

Provides learning algorithms and models for DecisionTree regression and classification.

Provides learning algorithms and models for RandomForest and ExtraTrees regression and classification.

Provides classification, regression, impurity and ranking metrics.

The GPU-accelerated version of package CenterSpace.NMath. With a few minor exceptions, such as optional GPU configuration settings, the API is identical between CenterSpace.NMath.Premium and CenterSpace.NMath. If using at least .NET Framework 4.6.1 or .NET Core 2.0, we recommend using one of our... More information

Provides ensemble learning for regression and classification.

Provides learning algorithms and models for GradientBoost regression and classification.

A library for producing diffs of images with highlighting.

Provides learning algorithms and models for AdaBoost regression and classification.

Provides learning algorithms and models for neural net regression and classification.

Classes for data manipulation and statistical computation, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix... More information

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... More information

LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines. This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018. For more information: https://github.com/ccerhan/LibSVMsharp

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... More information

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... More information

Provides learning algorithms and models for XGBoost regression and classification.