摘要
Robotics&Machine Learning Daily News的一位新闻记者兼工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中呈现。根据来自巴西戈尼亚尼亚的新闻，NEWSRX记者报道，“在这项研究中，研究人员调查了一组向我收取税款的信息。总共评估了12种不同的信息（社会规范、简化、披露、以前参与、提醒和以前选择），并测试了它们的有效性。”我们的新闻记者从联邦大学戈亚斯的研究中获得了一句话：“这些信息被传递给了巴西四个州的违约微型企业家。从一个包含违约微型企业家信息的数据库中，数据来自秘书处(Sempe),采用均值检验和Logistic回归分析,随机森林,随机Logistic回归和朴素贝叶斯小部件被用来表明机器学习预测模型的稳健性。研究结果表明，员工的“简化”、“先前选择”和“提醒”等格式对梳理违约没有影响。然而，当与社会规范相一致时，“过去的选择”和“提醒”形式的信息增加了债务的支付。所使用的widgets表明了机器学习模型的良好拟合。随机森林工具证明了该模型的优越性，证明了该模型是稳健的，适合预测函数。研究结果为公共政策提供了一个有效的行动来减少债务拖欠的贡献TA XES的数量"
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news originating from Goiania, Brazil, by N ewsRx correspondents, research stated, “In this research, a group of charging me ssages for the payment of taxes was investigated. Altogether 12 variations of bi lling messages (social norms, simplification, disclosure, previous engagement, r eminders and previous choices) were evaluated, and their effectiveness was teste d.” Our news journalists obtained a quote from the research from Federal University Goias, “The messages were transmitted to defaulting microentrepreneurs in four B razilian states. From a database containing information about defaulting micro e ntrepreneurs, 250 thousand text messages were sent making charges. The data were obtained from the Secretaria Especial da Micro e Pequena Empresa (Sempe). Tests were used to analyse the difference between means and Logistic Regression was u sed in sequence. The Random Forest, Logistic Regression and Na & i uml;ve Bayes widgets were used to indicate the robustness of the Machine Learnin g predictive model. The research findings indicated that the formats ‘simplifica tion’, ‘previous choices’ and ‘alert’, employees, did not have an effect in comb ating default. However, when aligned with social norms, messages in the form of ‘past options’ and ‘reminders’ increase the payment of debts. The widgets used i ndicated an excellent fit to the machine learning model. The Random Forest tool attested with superiority that the model is robust and suitable for the predicti ve function. The results of the research provide a contribution to public polici es when they present an effective action to reduce defaults in the payment of ta xes.”
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