摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-详细介绍了人工智能ce的数据。根据NewsRx记者从法国Ecully发回的新闻报道，研究表明：“随着打电话服务的日益普及，使用人工智能(AI)建立透明和可解释的定价模型变得非常重要。虽然这一领域的文献稳步增长，但人工智能在定价预测中的应用相对较新。”我们的新闻编辑从埃里昂商学院的研究中获得了一句话，“我们利用纽约市出租车数据集建立了定价预测模型，以弥合这一差距。我们的贡献如下。首先，我们为黄色出租车和应用出租车创建了独特的聚类，从而在纽约市不同地区建立了动态定价机制。其次，第三，我们应用变重要性方案在每个聚类中生成最高预测Rs，第四，我们发现每个预测因子对出租车定价的影响在不同聚类中存在差异。第五，“拥挤离子附加费”仅对少数聚类有显著意义。"实施这样的管制可能会损害整个出租车行业."
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 originating from Ecully, Fra nce, by NewsRx correspondents, research stated, "With increasing popularity of r ide -hailing services, it becomes important to build transparent and explainable pricing models using artificial intelligence (AI). While the literature on this domain is growing steadily, the application of AI in pricing prediction is rela tively new." Our news editors obtained a quote from the research from EMLYON Business School, "We drew upon the New York City Taxi dataset to build pricing prediction models to bridge this gap. Our contributions are as follows. First, we created unique clusters for yellow and app -based cabs, leading to a dynamic pricing mechanism across different zones in New York City. Second, we converted a prediction probl em into a classification problem by transforming the prices into four distinct q uartiles. Third, we applied variable importance schemes to generate top predicto rs in each cluster. Fourth, our study reveals that differential effects of each predictor for cab -pricing across different clusters exist. Fifth, the ‘ congest ion surcharge ‘ is significant for only a few clusters, and imposing such surcha rges could hurt the overall taxicab industry."
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