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160 packages
returned for Tags:"linear-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...

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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...

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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...

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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...

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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...

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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...

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Extreme Optimization Numerical Libraries for .NET Single-Precision P/Invoke MKL Provider for Linux.

Extreme Optimization Numerical Libraries for .NET P/Invoke MKL Provider for Linux.

Provides learning algorithms and models for DecisionTree 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...

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The Syncfusion Carousel control for Xamarin.Froms provides an intuitive interface to navigate through a collection of views with scaling and rotation transformations. Linear interface allows navigation through a collection of views without scaling and rotation transformations.
Key features:...

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Linear regression classification

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

Provides classification, regression, impurity and ranking metrics.

Syncfusion Gauge for Xamarin.Forms is a data visualization component that helps display numerical values. The appearance of the gauge can be fully customized to seamlessly integrate with your applications.
Note: This package needs to be installed in all Xamarin.Forms projects (PCL/.NET Standard,...

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.NET Library for calculations in the area of predictive analytics. It contains relevant methods of the machine learning context such as nonlinear regressions, clustering and multivariate statistics.

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...

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