Prediction Maps & Validation using Logistic Regression & ROC

Unlock the Secrets of Hazard Prediction with Dr. Omar AlThuwaynee's Course: "Prediction Maps & Validation using Logistic Regression & ROC"

🎉 Course Title: Master the Art of Hazard Prediction and ROC Validation with Logistic Regression in GIS and R 📊✨

Course Description:

Embark on a Journey from Prediction to ROC Validation Mastery

Welcome to an enlightening journey through the world of geographic information systems (GIS), where Dr. Omar AlThuwaynee guides you step-by-step in evaluating and comparing the results of applying logistic regression for hazard prediction mapping using GIS and R environment.

Stay Focused on Logistic Regression:

• Understand the nuances of logistic regression, a binary classification method used to determine whether a certain event will occur in a specific location or not.
• Dive into the mechanics of fitting a regression curve using logistic regression, especially useful when dealing with categorical output variables.
• Explore why logistic regression stands out: it applies a sigmoid function, which is essentially a mini neural network!

Real-World Application:

• Utilize experimental data consisting of 75 observations of independent factor Y (Landslide training data locations) and dependent factors X (Elevation, slope, NDVI, Curvature, and landcover).
• Discover the spatial correlation between prediction factors and the dependent factor, including how to identify autocorrelations between prediction factors while considering their predictive importance or contribution.
• Learn to produce a susceptibility map using R studio and ESRI ArcGIS with ease.

Validation Techniques:

• Master model prediction validation using one of the most common statistical methods: the Area Under (AUC) the ROC curve, which is crucial for assessing model performance in predictive tasks.

Course Outline:

• Understanding Logistic Regression: A comprehensive introduction to binary classification and logistic regression as a special case of linear regression for categorical output variables.

• Data Preparation: Learn how to prepare your data, understand the significance of each independent variable, and how they relate to the dependent factor.

• Spatial Analysis: Explore spatial correlations and autocorrelations between prediction factors and their influence on the prediction's reliability.

• Map Production: A hands-on approach to creating susceptibility maps using R studio and ESRI ArcGIS, tailored for users with varying skill levels.

• Model Validation: Measure your model’s predictive capability through the ROC curve and AUC analysis, ensuring you can interpret and apply these metrics in real-world scenarios.

What You Will Learn:

By the end of this course, you will be proficient in:

• Processing and analyzing geographic data for hazard prediction research.
• Applying advanced logistic regression analysis to your datasets.
• Predicting outcomes with high accuracy using R studio and GIS tools.
• Validating your predictions using ROC curve analysis, a key technique in statistical modeling.

Keywords:

• R studio
• GIS
• Logistic Regression
• Mapping
• Prediction
• Hazard Prediction
• Spatial Analysis
• ROC Curve Validation

🛠️ Tools & Techniques: Get ready to leverage R studio, GIS software, logistic regression, spatial analysis, and ROC curve validation in your next project.

Join Dr. Omar AlThuwaynee Today and Elevate Your Skills in Predictive Mapping with Logistic Regression and ROC Validation! 🚀🌐

Whether you're a researcher, data scientist, or enthusiast in the field of natural hazards, this course will equip you with the skills to accurately predict, analyze, and validate your findings using state-of-the-art methods. Enroll now and transform your approach to geographic data analysis! 🌟