摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑每日新闻-关于人工智能ce的详细数据已经呈现。根据NewsRx编辑在普渡大学的新闻报道，研究表明，"机器学习模型有能力在合法评论(HAM)和垃圾邮件之间过滤Youtube视频评论的过程。"这项研究的财政支持者包括国家科学基金会。我们的新闻记者从普渡大学Y的研究中获得了一句话：“为了将机器学习模型整合到媒体传播平台上的常规使用中，最近的方法旨在开发受过Youtu Be评论培训的模型，这些模型已经成为分类的宝贵工具，并使识别垃圾内容和增强用户体验成为可能。”摘要:将8种机器学习方法应用于Y ouTube评论的垃圾邮件检测,其中包括高斯朴素贝叶斯、logistic回归、k近邻(KNN)分类器、(MLP)上的多层感知器、支持向量机(SVM)分类器、随机森林分类器、决策树分类器、投票分类器,8种机器学习模型均取得了良好的效果。具体而言，随机森林方法的平均精度为100%，AUC-ROC为0.9841."，几乎达到了理想的性能。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting out of Purdue University by NewsRx editors, research stated, “Machine Learning models have the ability to st reamline the process by which Youtube video comments are filtered between legiti mate comments (ham) and spam.” Financial supporters for this research include National Science Foundation. Our news correspondents obtained a quote from the research from Purdue Universit y: “In order to integrate machine learning models into regular usage on media-sh aring platforms, recent approaches have aimed to develop models trained on Youtu be comments, which have emerged as valuable tools for the classification and hav e enabled the identification of spam content and enhancing user experience. In t his paper, eight machine learning approaches are applied to spam detection for Y ouTube comments. The eight machine learning models include Gaussian Naive Bayes, logistic regression, K-nearest neighbors (KNN) classifier, multi-layer perceptr on (MLP), support vector machine (SVM) classifier, random forest classifier, dec ision tree classifier, and voting classifier. All eight models perform very well , specifically random forest approach can achieve almost perfect performance wit h average precision of 100% and AUC-ROC of 0.9841.”
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