Predicting popularity of online articles using Random Forest regression

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI:10.1109/CCIP.2016.7802890

R. Shreyas, D. Akshata, B. S. Mahanand, B. Shagun, C. Abhishek

引用次数: 22

Abstract

Predictive analysis using machine learning has been gaining popularity in recent times. In this paper, the Random Forest regression model is used to predict popularity of articles from the Online News Popularity data set. The performance of the Random Forest model is investigated and compared with other models. Impact of standardization, regularization, correlation, high bias/high variance and feature selection on the learning models are also studied. Results indicate that, the Random Forest approach predicts popular/unpopular articles with an accuracy of 88.8%.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

使用随机森林回归预测网络文章的受欢迎程度

近年来，使用机器学习的预测分析越来越受欢迎。本文采用随机森林回归模型对在线新闻流行度数据集中文章的流行度进行预测。研究了随机森林模型的性能，并与其他模型进行了比较。研究了标准化、正则化、相关性、高偏差/高方差和特征选择对学习模型的影响。结果表明，随机森林方法预测流行/不流行文章的准确率为88.8%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)

自引率

0.00%

发文量

期刊最新文献

Hilbert Huang transform based speech recognition An Integrated curvature and Convex Hull based concave point detection in Histopathological images Study of Complex-valued Learning algorithms for Post-surgery survival prediction Optimized Radial Basis Function Neural Network model for wind power prediction Early detection of grapes diseases using machine learning and IoT