Libraries tagged by regressions

rubix/ml

1981 Favers
631701 Downloads

A high-level machine learning and deep learning library for the PHP language.

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markrogoyski/math-php

2317 Favers
3755300 Downloads

Math Library for PHP. Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra

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codeception/visualception

87 Favers
594118 Downloads

Visual regression tests for Codeception

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mcordingley/regression

88 Favers
67817 Downloads

Regression Package for PHP

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dr-que/polynomial-regression

22 Favers
152062 Downloads

Calculates and returns coefficients for polynomial regression.

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robotomize/regression-php

16 Favers
29659 Downloads

Calculates regression

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mnshankar/linear-regression

24 Favers
19216 Downloads

PHP package for computation of simple linear regression parameters

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marrouchi/polynomial-regression

1 Favers
25993 Downloads

DeQue Polynomial Regression is a PHP library created by Andrew Que. Adapted to use autoloading.

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denissimon/prediction-builder

111 Favers
6673 Downloads

A library for machine learning that builds predictions using a linear regression.

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beyondcode/laravel-visual-diff

161 Favers
29469 Downloads

Create visual diffs in your application tests.

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stitch/regression-php

1 Favers
8094 Downloads

regression-php is a Php component containing a collection of linear least-squares fitting methods for simple data analysis.

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saschaegerer/css-regression

5 Favers
632 Downloads

CSS Regression Module for Codeception

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wellingtonbarbosa/knn

6 Favers
16 Downloads

A package to calculate Euclidean distance in a simple way, in addition to comparing results for recommendation quickly and symptomatically, without ifs.

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rubix/housing

45 Favers
379 Downloads

An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

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rubix/credit

27 Favers
184 Downloads

An example project that predicts the risk of credit card default using a Logistic Regression classifier and a 30,000 sample dataset of credit card customers.

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