Prediction Maps & Validation using Logistic Regression & ROC | Science Online Course by Udemy

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Comprehensive (Step-by-Step) Procedure From Prediction to ROC Validation of Maps using Logistic Regression In GIS and R

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Prediction Maps & Validation using Logistic Regression & ROC

In the this course,i have shared complete process(A to Z) based on my published articles, abouthow 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 beenfacilitated 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 factorwill 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 its 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 theautocorrelations between; the prediction factors, by considering their prediction importance or contribution. Finally, I willProduce susceptibility map using; R studio and ESRI ArcGISonly. Model predictionvalidation will be measured by most common statistical method of Area under (AUC) the ROCcurve. At the the end ofthis course, you will be efficientlyable to process, predict and validateany sort of data related tonatural scienceshazard research, using advanced Logistic regression analysis capability. Keywords: R studio, GIS, Logistic regression, Mapping, Prediction

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