Application Of Machine Learning (Linear Regression Model) To Predict Students Enrollment Among Senior High Schools In GhanaOsei Wusu Brempong Jnr
The perfor- mance of these machine learning methods will be compared against baseline traditional approaches including naı ¨ve forecasting, moving average, linear regression, and time series models. We have included two sources of distorted demand data for our analysis. The first source is ...
This paper presents two recent machine learning techniques namely Model Tree (MT) and Gene Expression Programming (GEP) to predict suspended sediment loads (SSL) in river. Mt is kind of decision tree, which has the capability to predict the numeric values with linear regression function at the ...
Moreover, these models show good predictive performance with R 2 exceeding 0.94. Comparative analysis is also done to evaluate the predictability of these models. 展开 关键词: Production prediction Data-driven techniques Machine learning Support vector regression Neural networks Particle swarm optimization...
study involving both homogenous and heterogeneous reservoir model, Further, we use various machine learning regression approach that lies within the domain of ADP to directly estimate the conditional expected value given the data outcomes without approximating the posterior probabilities of reservoir ...
The first method used for this purpose was principal component analysis (PCA) which resulted in development of a set of new empirical equations. Also, two Soft computing techniques including adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR) have been employed for ...
Similarly, the authors also have implemented various supervised ML algorithms like SVM, K-NN, Decision tree, Naïve Bayes, Regression analysis with ANN and ANFIS. Based on accuracy rate, prediction speed, and training time, algorithms are compared to find out most suitable algorithm for this exp...
Most of our familiar statistical methods, such as hypothesis testing, linear regression, analysis of variance, and maximum likelihood estimation, were desi... B Efron,RJ Tibshirani - 《Science》 被引量: 3320发表: 1991年 A Brief Survey of Machine Learning Methods for Classification in Networked Da...
In recent years,deep learning has been widely used in diverse fields of research,such as speech recognition,image classification,autonomous driving and natural language processing.Deep learning has showcased dramatically improved performance in complex classification and regression problems,where the intricate...
Our findings suggest that while recurrent neural networks and support vector machines show the best performance, their forecasting accuracy was not statistically significantly better than that of the regression model. 展开 关键词: Practical/ demand forecasting recurrent neural nets regression analysis supply...