Overview Understanding key machine learning algorithms is crucial for solving real-world data problems effectively.Data ...

Various Machine Learning models such as Linear Regression, Logistic Regression, Random Forests and Deep Learning will be introduced to fit and classify biomedical data. Unsupervised learning ...

Logistic regression, nearest neighbors regression, random forest regression (RFR), support vector regression, and K-nearest neighbors regression were the machine-learning algorithms used for this ...

Once identified, these abnormal returns can be forecast by classification models such as logistic regression, random forests, or gradient boosting machines, which make use of liquidity measures ...

For the machine-learning analysis, they compared standard multivariable logistic regression, LASSO logistic regression, Random Forest and Extreme Gradient Boosting.

In this context, using prognostic modelling approaches through machine learning technics emerges as a promising solution. Objectives We aim to assess the ability of several machine learning techniques ...

EHR data may be particularly suitable for machine learning (ML) techniques, as such algorithms can process high-dimensional data and capture nonlinear relationships between variables. By comparison, ...

Methods We trained models using logistic regression (LR) and four commonly used ML algorithms to predict NCGC from age-/sex-matched controls in two EHR systems: Stanford University and the University ...