Understanding the Least Squares Criterion for Accurate Data Fitting

Learn how the Least Squares Criterion determines the line of best fit for data analysis, enhancing predictive accuracy in finance, economics, and investing.
eLife

Global molecular landscape of early MASLD progression in obesity

The authors provide a useful integrated analytical approach to investigating MASLD focused on diverse multiomic integration methods. The strength of evidence for this new resource is solid, as ...
Scientific Research Publishing

Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms ()

Felimban, R. (2025) Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms.
IEEE

Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models

Abstract: Mixed linear regression (MLR) models nonlinear data as a mixture of linear components. When noise is Gaussian, the Expectation-Maximization (EM) algorithm is commonly used for maximum ...
IEEE

An autoML algorithm to select linear regression model and its performance evaluation for linear datasets

Abstract: This paper presents an autoML algorithm to select linear regression model and its performance evaluation for any linear dataset. It computes and compares the performance of various multiple ...
Frontiers

Handling missing data of using the XGBoost-based multiple imputation by chained equations regression method

1 Shangwan Coal Mine, Ejin Horo Banner, Ordos, China 2 CCTEG Xi’an Research Institute Co. Ltd., Xi’an, China This study introduces an XGBoost-MICE (Multiple Imputation by Chained Equations) method for ...
GitHub

A scikit-learn compatible implementation of the Adaptive Lasso algorithm for feature selection and regression.

Adaptive Lasso is an extension of the standard Lasso method that provides improved feature selection properties through weighted L1 penalties. It assigns different weights to different coefficients in ...