An accurate prediction of house prices is a fundamental requirement for
various sectors including real estate and mortgage lending. It is widely
recognized that a property value is not solely determined by its physical
attributes but is significantly influenced by its surrounding neighbourhood.
Meeting the diverse housing needs of individuals while balancing budget
constraints is a primary concern for real estate developers. To this end, we
addressed the house price prediction problem as a regression task and thus
employed various machine learning techniques capable of expressing the
significance of independent variables. We made use of the housing dataset of
Ames City in Iowa, USA to compare support vector regressor, random forest
regressor, XGBoost, multilayer perceptron and multiple linear regression
algorithms for house price prediction. Afterwards, we identified the key
factors that influence housing costs. Our results show that XGBoost is the best
performing model for house price prediction.

房屋价格的准确预测是包括房地产和抵押贷款在内各个行业的基本要求。本研究通过运用各种机器学习技术来解决房价预测问题，并使用爱荷华州艾姆斯市的房屋数据集比较了支持向量回归器、随机森林回归器、XGBoost、多层感知器和多元线性回归算法。最终确定了影响房屋成本的关键因素，并得出结果表明 XGBoost 是最佳的房价预测模型。

最佳房价预测算法：XGBoost

An Optimal House Price Prediction Algorithm: XGBoost

Recent years have witnessed the significant advance in dense retrieval (DR)
based on powerful pre-trained language models (PLM). DR models have achieved
excellent performance in several benchmark datasets, while they are shown to be
not as competitive as traditional sparse retrieval models (e.g., BM25) in a
zero-shot retrieval setting. However, in the related literature, there still
lacks a detailed and comprehensive study on zero-shot retrieval. In this paper,
we present the first thorough examination of the zero-shot capability of DR
models. We aim to identify the key factors and analyze how they affect
zero-shot retrieval performance. In particular, we discuss the effect of
several key factors related to source training set, analyze the potential bias
from the target dataset, and review and compare existing zero-shot DR models.
Our findings provide important evidence to better understand and develop
zero-shot DR models.

本文通过第一次全面细致的研究，发现了与源数据集相关的关键因素、目标数据集的潜在偏差和现有零样本 DR 模型，从而提供了关于零样本 DR 模型的更好的理解和发展的重要证据。