重温电影经济成功的预测:随机森林应用于利
Thaís Luiza Donega E Souza1, Marislei Nishijima2, Ricardo Pires3
1Information Systems Department, University of São Paulo, 1000, Arlindo Béttio - Ermelino Matarazzo, 03828-000, Room: L1 - 327, São Paulo, SP Brazil.
概括
机器学习模型使用通货膨胀调整后的利和早期生产数据预测电影的经济成功. 随机森林获得了97%的准确性,超过了以前的研究,以更好的电影制作决策.
科学领域:
- *计算经济学和娱乐行业分析.
- * 机器学习在财务预测中的应用.
背景情况:
- *以前的机器学习 (ML) 研究旨在预测电影的成功,减少生产的不确定性.
- *现有的模型经常使用名义的票房收入和发布后可用的数据.
研究的目的:
- *通过解决先前研究的局限性来改善电影经济成功分类.
- *分析数据采样 (时间,释放类型) 对预测准确性的影响.
- *使用无通胀的利指标和发布前数据,以提供更实用的生产者指导.
主要方法:
- *使用了来自Box Office Mojo的3167部公开预算的电影 (1980-2019) 的数据集.
- *使用随机森林,支持向量机和神经网络分类器.
- *使用不同的基于时间的数据样本和释放类型 (总和宽) 进行模型性能比较.
主要成果:
- *随机森林分类器表现出卓越的性能,达到97%的准确性和F1分数.
- *经通货膨胀调整后的利比名义收入更准确地衡量经济成功.
- *使用生产时可用的功能增强了模型的实用性.
结论:
- * 机器学习,特别是随机森林,可以有效地利用早期数据和经通胀调整的利预测电影的经济成功.
- *这些发现为生产者提供了宝贵的见解,以减轻财务风险和优化决策.
- *与以前的方法相比,这项研究为预测电影成功提供了更强大和更实用的方法.
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