空间意识机器学习模型
The Spatially-Conscious Machine Learning Model
摘要：
成功预测中产阶级化可能有许多社会和商业应用;然而，房地产销售很难预测，因为它们属于一个由内在和外在特征，感知价值和市场投机组成的混沌系统。以纽约市房地产为主题，我们将数据科学和机器学习的现代技术与传统的空间分析相结合，为分类和回归任务创建强大的房地产预测模型。我们比较了几个尖端的机器学习算法在空间，半空间和非空间特征工程技术，我们的经验表明，空间意识的机器学习模型优于非空间模型时，结合先进的预测技术，如前馈人工神经网络和梯度提升机器模型。
英文摘要：
Successfully predicting gentrification could have many social and commercial applications; however, real estate sales are difficult to predict because they belong to a chaotic system comprised of intrinsic and extrinsic characteristics, perceived value, and market speculation. Using New York City real estate as our subject, we combine modern techniques of data science and machine learning with traditional spatial analysis to create robust real estate prediction models for both classification and regression tasks. We compare several cutting edge machine learning algorithms across spatial, semi-spatial and non-spatial feature engineering techniques, and we empirically show that spatially-conscious machine learning models outperform non-spatial models when married with advanced prediction techniques such as feed-forward artificial neural networks and gradient boosting machine models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
