Today, as I delve deeper into my project involving Centers for Disease Control and Prevention (CDC) data on diabetes, obesity, and physical activity rates across US counties, I’m excited to share the progress I’ve made with a special focus on Linear Regression.
Linear Regression Recap
In my exploration of this extensive dataset, I’ve already embarked on the path of Linear Regression. I carefully examined the data, and it’s heartening to report that no significant evidence has surfaced during my analysis.
Cross-Validation:
One crucial tool that’s guiding me through this project is Cross-Validation. Imagine it as a set of compasses, helping me navigate through the complex terrain of data analysis. Cross-Validation is my compass that allows me to assess the effectiveness of my predictive models. It helps me gauge how well my models generalize to new data, a critical aspect of any robust analysis.
The fundamental idea behind Cross-Validation is to divide my dataset into multiple subsets or “folds.” Each fold serves as a unique test set while the remaining folds are used for training. By rotating through these combinations, I obtain a more accurate evaluation of my model’s performance. K-Fold Cross-Validation is the most commonly used method, dividing the data into K nearly equal-sized folds.