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所在平台: Udemy |
课程主页: https://www.udemy.com/course/a-to-z-prediction-mapping-using-logistic-regression-in-gis/
课程评论：没有评论
课程名称：使用逻辑回归和ROC进行预测图与验证 课程概述：本课程详细介绍了基于所发布文章的完整流程，旨在评估和比较多元逻辑回归方法在使用GIS和R环境进行危险预测制图中的应用结果。过去十年间，地理信息系统（GIS）的发展促进了新的机器学习、数据驱动和实证方法的出现，这些方法能够减少泛化误差，并为综合研究领域带来了新的维度。 逻辑回归的重点在于二元分类，主要用于拟合回归曲线，特别是在输出变量为分类时的线性回归特例，其中我们将对数几率作为因变量。逻辑回归的独特之处在于它对特征的线性组合应用非线性函数（sigmoid），因此可以看作是神经网络的一个简单实例。 在课程中，我使用了实验数据，包括：自变量Y（滑坡训练数据位置）75个观察值；因变量X（高程、坡度、NDVI、曲率和土地覆盖）。我将解释预测因子与因变量之间的空间相关性，以及如何通过考虑它们的预测重要性或贡献来查找预测因子之间的自相关性。最终，我将使用R Studio和ESRI ArcGIS制作危险性图。模型预测的验证将通过常用的统计方法——ROC曲线下的面积（AUC）来衡量。 课程结束时，您将能够高效处理、预测和验证与自然科学危险研究相关的各种数据，利用先进的逻辑回归分析能力。 关键词：R Studio、GIS、逻辑回归、映射、预测
In the this course, i have shared complete process (A to Z ) based on my published articles, about how to evaluate and compare the results of applying the multivariate logistic regression method in Hazard prediction mapping using GIS and R environment.Since last decade, geographic information system (GIS) has been facilitated the development of new machine learning, data-driven, and empirical methods that reduce generalization errors. Moreover, it gives new dimensions for the integrated research field.STAY FOCUSED: Logistic regression (binary classification, whether dependent factor will occur (Y) in a particular places, or not) used for fitting a regression curve, and it is a special case of linear regression when the output variable is categorical, where we are using a log of odds as the dependent variable. Why logistic regression is special? It takes a linear combination of features and applies a nonlinear function (sigmoid) to it, so it's a tiny instance of the neural network!In the current course, I used experimental data that consist of: Independent factor Y (Landslide training data locations) 75 observations; Dependent factors X (Elevation, slope, NDVI, Curvature, and landcover)I will explain the spatial correlation between; prediction factors, and the dependent factor. Also, how to find the autocorrelations between; the prediction factors, by considering their prediction importance or contribution. Finally, I will Produce susceptibility map using; R studio and ESRI ArcGIS only. Model prediction validation will be measured by most common statistical method of Area under (AUC) the ROC curve.At the the end of this course, you will be efficiently able to process, predict and validate any sort of data related to natural sciences hazard research, using advanced Logistic regression analysis capability.Keywords: R studio, GIS, Logistic regression, Mapping, Prediction
