StatsLearning Chapter 6 part 1

Linear model selection, in this course introduces you to the key techniques used to identify the best linear regression models for predictive accuracy and interpretability. You'll explore methods such as best subset selection, forward selection, and backward elimination. We’ll dive into how to evaluate models using criteria like Adjusted R², AIC, BIC, and cross-validation. The course emphasizes balancing model complexity with performance, helping you avoid overfitting and underfitting. Through hands-on examples using R or Python, you’ll gain practical experience in building and comparing multiple models. Whether you are working with a small set of features or facing high-dimensional data, you’ll learn strategies to select the most relevant predictors. By the end of the course, you’ll be equipped with the knowledge to apply model selection techniques effectively in real-world data science and statistical modeling tasks. Data School