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Regression Analysis with Scikit-Learn (part 1 - Linear)

The Programming Historian, 2022
This lesson is the first of a two-part lesson focusing on an indispensable set of data analysis methods, logistic and linear regression. It provides an overview of linear regression and walks through running both algorithms in Python (using scikit-learn).
Matthew J. Lavin
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Function-on-function linear quantile regression

Mathematical Modelling and Analysis, 2022
In this study, we propose a function-on-function linear quantile regression model that allows for more than one functional predictor to establish a more flexible and robust approach. The proposed model is first transformed into a finitedimensional space
Ufuk Beyaztas, Han Lin Shang
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Adiabatic quantum linear regression

Scientific Reports, 2021
A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on ...
Prasanna Date, Thomas Potok
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Post-processing through linear regression [PDF]

Nonlinear Processes in Geophysics, 2011
Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed,
B. Van Schaeybroeck, S. Vannitsem
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Knowledge and Awareness: Linear Regression [PDF]

Educational Process: International Journal, 2016
Knowledge and awareness are factors guiding development of an individual. These may seem simple and practicable, but in reality a proper combination of these is a complex task.
Monika Raghuvanshi
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Secure Collaborative Computing for Linear Regression

Applied Sciences, 2023
Machine learning usually requires a large amount of training data to build useful models. We exploit the mathematical structure of linear regression to develop a secure and privacy-preserving method that allows multiple parties to collaboratively compute
Albert Guan, Chun-Hung Lin, Po-Wen Chi
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An improved quantum-inspired algorithm for linear regression [PDF]

Quantum, 2022
We give a classical algorithm for linear regression analogous to the quantum matrix inversion algorithm [Harrow, Hassidim, and Lloyd, Physical Review Letters'09] for low-rank matrices [Wossnig, Zhao, and Prakash, Physical Review Letters'18], when the ...
András Gilyén, Zhao Song, Ewin Tang
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Privacy-preserving Linear Regression Scheme and Its Application [PDF]

Jisuanji kexue, 2022
Linear regression is an important and widely used machine learning algorithm.The training of linear regression model usually depends on a large amount of data.In reality,the data set is generally held by different users and contains their privacy ...
LYU You, WU Wen-yuan
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Tensor Linear Regression: Degeneracy and Solution

IEEE Access, 2021
Tensor regression is an important and useful tool for analyzing multidimensional array data. To deal with high dimensionality, CANDECOMP/PARAFAC (CP) low-rank constraints are often imposed on the coefficient tensor parameter in the (penalized) loss ...
Ya Zhou, Raymond K. W. Wong, Kejun He
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Linear regression analysis study

Journal of the Practice of Cardiovascular Sciences, 2018
Linear regression is a statistical procedure for calculating the value of a dependent variable from an independent variable. Linear regression measures the association between two variables.
Khushbu Kumari, Suniti Yadav
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