… linear regression and polynomial regression in this paper that better fits the predictive
model[16]. Regression [17] may either be a simple linear regression or multiple regression. …

… regression analysis model based on the cleansed data by means of data mining theory.
Regression … Linear regression model that corresponds to the practical situation is proposed in …

… regression models which were the main focus of the study performed fairly well in comparison
to other more advanced machine learning … Regression Trees is another machine learning …

… Consistency does not hold for logistic regression (logreg), … of machine learning techniques
(MLT) and regression models (… role of MLT in addition to regression modeling (Section 4). …

… of advanced ML regressions in two EMC applications. Specifically, support vector machine,
least-squares support vector machine and Gaussian process regressions are adopted to …

… factors, and various machine learning algorithms have different effects on … machine learning
algorithm in regression problem, this paper mainly adopts three common machine learning …

… This paper aims to provide a better understanding of how various Machine Learning
models can be implemented in real-world situations. Apart from the analysis done on the world …

… prior machine learning … learning to solve the problem of predicting performance of specific
algorithms on specific datasets. We apply both linear regression and non-linear regression. …

… of the machine-learning algorithm and UM regression model … The UM regression model was
validated using data from the … Machine-learning algorithms do not need cross-validation or …

… Several recent studies investigated machine learning (ML) … how to employ ML regression
approaches to estimate the … the performance of three regression approaches by leveraging …