评估监督机器学习算法对钻石定价模型的预测性能
Samuel Njoroge Kigo1, Evans Otieno Omondi2,3, Bernard Oguna Omolo1,4,5
1Institute of Mathematical Sciences, Strathmore University, P.O Box 59857-00200, Nairobi, Kenya.
Scientific reports
|October 12, 2023
概括
随机森林 (RF) 模型准确预测钻石价格,优于其他机器学习算法. 这项研究突出了RF频率.
科学领域:
- 机器学习应用 机器学习应用
- 数据科学数据科学数据科学
- 计量经济学 计量经济学
背景情况:
- 钻石定价是复杂的,原因是卡拉特,切割,清晰度,表格和深度等特征之间的非线性关系.
- 准确的价格预测对钻石行业至关重要,影响定价策略和市场分析.
- 监督机器学习为建模这些复杂的关系提供了潜在的解决方案.
研究的目的:
- 为准确的钻石价格预测,全面分析和比较多个监督机器学习模型 (回归器和分类器).
- 确定最有效的模型来预测钻石价格,考虑回归和分类方法.
- 提供关于数据预处理技术对于提高预测模型性能的重要性的见解.
主要方法:
- 数据预处理包括异常值处理 (四分位数范围),标准化,缺失值的中位数归算和多对线性解析.
- 特征工程涉及"切割"变量和基于关联的特征选择的等宽分区.
- 评估的模型包括随机森林 (RF),多层感知器 (MLP),XGBoost,增强决策树,K-最近邻居 (KNN),线性回归和支持向量回归.
主要成果:
- 随机森林 (RF) 回归器实现了最低的根平均平方误差 (RMSE) 523.50和最高的R平方得分0.985.50.
- 射频分类器表现出完美的性能,曲线下的面积 (AUC) 为1.00.
- 其他模型,如MLP,XGBoost和增强决策树,显示出不同程度的有效性,而KNN,线性回归和支持矢量回归的性能不那么准确.
结论:
- 随机森林 (RF) 模型是准确预测钻石价格的最佳选择,在回归和分类任务中表现出色.
- 该研究强调了强大的数据预处理技术在提高价格预测机器学习模型准确性的重要性.
- 这些发现为钻石行业提供了有价值的工具和见解,有助于定价策略,市场趋势分析和知情决策.
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